def test_merge_informative_pair_lower():
data = generate_test_data('1h', 40)
informative = generate_test_data('15m', 40)
with pytest.raises(ValueError,
match=r"Tried to merge a faster timeframe .*"):
merge_informative_pair(data, informative, '1h', '15m', ffill=True)
def test_merge_informative_pair():
data = generate_test_data('15m', 40)
informative = generate_test_data('1h', 40)
result = merge_informative_pair(data, informative, '15m', '1h', ffill=True)
assert isinstance(result, pd.DataFrame)
assert len(result) == len(data)
assert 'date' in result.columns
assert result['date'].equals(data['date'])
assert 'date_1h' in result.columns
assert 'open' in result.columns
assert 'open_1h' in result.columns
assert result['open'].equals(data['open'])
assert 'close' in result.columns
assert 'close_1h' in result.columns
assert result['close'].equals(data['close'])
assert 'volume' in result.columns
assert 'volume_1h' in result.columns
assert result['volume'].equals(data['volume'])
# First 3 rows are empty
assert result.iloc[0]['date_1h'] is pd.NaT
assert result.iloc[1]['date_1h'] is pd.NaT
assert result.iloc[2]['date_1h'] is pd.NaT
# Next 4 rows contain the starting date (0:00)
assert result.iloc[3]['date_1h'] == result.iloc[0]['date']
assert result.iloc[4]['date_1h'] == result.iloc[0]['date']
assert result.iloc[5]['date_1h'] == result.iloc[0]['date']
assert result.iloc[6]['date_1h'] == result.iloc[0]['date']
# Next 4 rows contain the next Hourly date original date row 4
assert result.iloc[7]['date_1h'] == result.iloc[4]['date']
assert result.iloc[8]['date_1h'] == result.iloc[4]['date']
informative = generate_test_data('1h', 40)
result = merge_informative_pair(data, informative, '15m', '1h', ffill=False)
# First 3 rows are empty
assert result.iloc[0]['date_1h'] is pd.NaT
assert result.iloc[1]['date_1h'] is pd.NaT
assert result.iloc[2]['date_1h'] is pd.NaT
# Next 4 rows contain the starting date (0:00)
assert result.iloc[3]['date_1h'] == result.iloc[0]['date']
assert result.iloc[4]['date_1h'] is pd.NaT
assert result.iloc[5]['date_1h'] is pd.NaT
assert result.iloc[6]['date_1h'] is pd.NaT
# Next 4 rows contain the next Hourly date original date row 4
assert result.iloc[7]['date_1h'] == result.iloc[4]['date']
assert result.iloc[8]['date_1h'] is pd.NaT
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
assert (timeframe_to_minutes(
self.timeframe) == 5), "Run this strategy at 5m."
if self.timeframe == self.informative_timeframe:
dataframe = self.slow_tf_indicators(dataframe, metadata)
else:
assert self.dp, "DataProvider is required for multiple timeframes."
informative = self.dp.get_pair_dataframe(
pair=metadata['pair'], timeframe=self.informative_timeframe)
informative = self.slow_tf_indicators(informative.copy(), metadata)
dataframe = merge_informative_pair(dataframe,
informative,
self.timeframe,
self.informative_timeframe,
ffill=True)
# don't overwrite the base dataframe's OHLCV information
skip_columns = [
(s + "_" + self.informative_timeframe)
for s in ['date', 'open', 'high', 'low', 'close', 'volume']
]
dataframe.rename(columns=lambda s: s.replace(
"_{}".format(self.informative_timeframe), "")
if (not s in skip_columns) else s,
inplace=True)
dataframe = self.fast_tf_indicators(dataframe, metadata)
return dataframe
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:
"""
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.
"""
dataframe['ema20'] = ta.EMA(dataframe, timeperiod=20)
dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
if self.dp:
# Get ohlcv data for informative pair at 15m interval.
inf_tf = '15m'
informative = self.dp.get_pair_dataframe(pair=f"BTC/USDT",
timeframe=inf_tf)
# calculate SMA20 on informative pair
informative['sma20'] = informative['close'].rolling(20).mean()
# Combine the 2 dataframe
# This will result in a column named 'closeETH' or 'closeBTC' - depending on stake_currency.
dataframe = merge_informative_pair(dataframe,
informative,
self.timeframe,
inf_tf,
ffill=True)
return dataframe
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
if self.config['runmode'].value in ('backtest', 'hyperopt'):
assert (timeframe_to_minutes(self.timeframe) <=
5), "Backtest this strategy in 5m or 1m timeframe."
if self.timeframe == self.informative_timeframe:
dataframe = self.do_indicators(dataframe, metadata)
else:
if not self.dp:
return dataframe
informative = self.dp.get_pair_dataframe(
pair=metadata['pair'], timeframe=self.informative_timeframe)
informative = self.do_indicators(informative.copy(), metadata)
dataframe = merge_informative_pair(dataframe,
informative,
self.timeframe,
self.informative_timeframe,
ffill=True)
# don't overwrite the base dataframe's OHLCV information
skip_columns = [
(s + "_" + self.informative_timeframe)
for s in ['date', 'open', 'high', 'low', 'close', 'volume']
]
dataframe.rename(columns=lambda s: s.replace(
"_{}".format(self.informative_timeframe), "")
if (not s in skip_columns) else s,
inplace=True)
return dataframe
def slow_tf_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
dataframe5m = self.dp.get_pair_dataframe(pair=metadata['pair'],
timeframe="5m")
create_ichimoku(dataframe5m,
conversion_line_period=355,
displacement=880,
base_line_periods=175,
laggin_span=175)
dataframe5m['hma888'] = ftt.hull_moving_average(dataframe5m, 888)
dataframe = merge_informative_pair(dataframe,
dataframe5m,
self.timeframe,
"5m",
ffill=True)
dataframe['hma100'] = ftt.hull_moving_average(dataframe, 100)
dataframe['ema550'] = ta.EMA(dataframe, timeperiod=550)
dataframe['ema633'] = ta.EMA(dataframe, timeperiod=633)
create_ichimoku(dataframe,
conversion_line_period=20,
displacement=88,
base_line_periods=88,
laggin_span=88)
create_ichimoku(dataframe,
conversion_line_period=9,
displacement=26,
base_line_periods=26,
laggin_span=52)
create_ichimoku(dataframe,
conversion_line_period=444,
displacement=444,
base_line_periods=444,
laggin_span=444)
create_ichimoku(dataframe,
conversion_line_period=355,
displacement=888,
base_line_periods=175,
laggin_span=175)
dataframe['ichimoku_ok'] = (
(dataframe['kijun_sen_355_5m'] >= dataframe['tenkan_sen_355_5m']) &
(dataframe['kijun_sen_20'] > dataframe['tenkan_sen_444']) &
(dataframe['senkou_a_9'] > dataframe['senkou_a_20']) &
(dataframe['tenkan_sen_20'] >= dataframe['kijun_sen_20']) &
(dataframe['tenkan_sen_9'] >= dataframe['tenkan_sen_20']) &
(dataframe['tenkan_sen_9'] >= dataframe['kijun_sen_9'])
).astype('int') * 4
dataframe['trending_over'] = ((dataframe['senkou_b_355_5m'] >
dataframe['close'])).astype('int') * 1
dataframe.loc[(dataframe['ichimoku_ok'] > 0), 'trending'] = 3
dataframe.loc[(dataframe['trending_over'] > 0), 'trending'] = 0
dataframe['trending'].fillna(method='ffill', inplace=True)
return dataframe
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
# Obelisk_Ichimoku_Slow does not use trailing stop or roi and should be safe to backtest at 5m
# if self.config['runmode'].value in ('backtest', 'hyperopt'):
# assert (timeframe_to_minutes(self.timeframe) <= 5), "Backtest this strategy in 5m or 1m timeframe."
if self.timeframe == self.informative_timeframe:
dataframe = self.slow_tf_indicators(dataframe, metadata)
else:
assert self.dp, "DataProvider is required for multiple timeframes."
informative = self.dp.get_pair_dataframe(
pair=metadata['pair'], timeframe=self.informative_timeframe)
informative = self.slow_tf_indicators(informative.copy(), metadata)
dataframe = merge_informative_pair(dataframe,
informative,
self.timeframe,
self.informative_timeframe,
ffill=True)
# don't overwrite the base dataframe's OHLCV information
skip_columns = [
(s + "_" + self.informative_timeframe)
for s in ['date', 'open', 'high', 'low', 'close', 'volume']
]
dataframe.rename(columns=lambda s: s.replace(
"_{}".format(self.informative_timeframe), "")
if (not s in skip_columns) else s,
inplace=True)
dataframe = self.fast_tf_indicators(dataframe, metadata)
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'])
if self.config['runmode'].value in ('backtest', 'hyperopt'):
assert (timeframe_to_minutes(self.timeframe) <=
30), "Backtest this strategy in 5m or 1m timeframe."
if self.timeframe == self.informative_timeframe:
dataframe = self.do_indicators(dataframe, metadata)
else:
if not self.dp:
return dataframe
informative = self.dp.get_pair_dataframe(
pair=metadata['pair'], timeframe=self.informative_timeframe)
informative = self.do_indicators(informative.copy(), metadata)
dataframe = merge_informative_pair(dataframe,
informative,
self.timeframe,
self.informative_timeframe,
ffill=True)
skip_columns = [(s + "_" + self.informative_timeframe) for s in [
'date', 'open', 'high', 'low', 'close', 'volume', 'emac',
'emao'
]]
dataframe.rename(columns=lambda s: s.replace(
"_{}".format(self.informative_timeframe), "")
if (not s in skip_columns) else s,
inplace=True)
# Slam some indicators into the trade_info dict so we can dynamic roi and custom stoploss in backtest
if self.dp.runmode.value in ('backtest', 'hyperopt'):
self.custom_trade_info[metadata['pair']]['roc'] = dataframe[[
'date', 'roc'
]].copy().set_index('date')
self.custom_trade_info[metadata['pair']]['atr'] = dataframe[[
'date', 'atr'
]].copy().set_index('date')
self.custom_trade_info[metadata['pair']]['sroc'] = dataframe[[
'date', 'sroc'
]].copy().set_index('date')
self.custom_trade_info[metadata['pair']]['ssl-dir'] = dataframe[[
'date', 'ssl-dir'
]].copy().set_index('date')
self.custom_trade_info[
metadata['pair']]['rmi-up-trend'] = dataframe[[
'date', 'rmi-up-trend'
]].copy().set_index('date')
self.custom_trade_info[
metadata['pair']]['candle-up-trend'] = dataframe[[
'date', 'candle-up-trend'
]].copy().set_index('date')
return dataframe
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
# Base Thiccness
dataframe['color'] = np.where(dataframe['close'] > dataframe['open'],
'green', 'red')
dataframe['how-thicc'] = (dataframe['close'] - dataframe['open']).abs()
dataframe['avg-thicc'] = dataframe['how-thicc'].abs().rolling(
36).mean()
dataframe['not-thicc'] = dataframe['how-thicc'] < (
dataframe['avg-thicc'])
dataframe['rly-thicc'] = dataframe['how-thicc'] > (
dataframe['avg-thicc'])
dataframe['xtra-thicc'] = np.where(
dataframe['rly-thicc'].rolling(8).sum() >= 5, 1, 0)
dataframe['roc'] = ta.ROC(dataframe, timeperiod=6)
informative = self.dp.get_pair_dataframe(pair=metadata['pair'],
timeframe=self.inf_timeframe)
informative['3d-low'] = informative['close'].rolling(72).min()
informative['3d-high'] = informative['close'].rolling(72).max()
dataframe = merge_informative_pair(dataframe,
informative,
self.timeframe,
self.inf_timeframe,
ffill=True)
return dataframe
def test_merge_informative_pair_same():
data = generate_test_data('15m', 40)
informative = generate_test_data('15m', 40)
result = merge_informative_pair(data, informative, '15m', '15m', ffill=True)
assert isinstance(result, pd.DataFrame)
assert len(result) == len(data)
assert 'date' in result.columns
assert result['date'].equals(data['date'])
assert 'date_15m' in result.columns
assert 'open' in result.columns
assert 'open_15m' in result.columns
assert result['open'].equals(data['open'])
assert 'close' in result.columns
assert 'close_15m' in result.columns
assert result['close'].equals(data['close'])
assert 'volume' in result.columns
assert 'volume_15m' in result.columns
assert result['volume'].equals(data['volume'])
# Dates match 1:1
assert result['date_15m'].equals(result['date'])
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 slow_tf_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
dataframe5m = self.dp.get_pair_dataframe(pair=metadata['pair'],
timeframe="5m")
create_ichimoku(dataframe5m,
conversion_line_period=355,
displacement=880,
base_line_periods=175,
laggin_span=175)
dataframe = merge_informative_pair(dataframe,
dataframe5m,
self.timeframe,
"5m",
ffill=True)
create_ichimoku(dataframe,
conversion_line_period=20,
displacement=88,
base_line_periods=88,
laggin_span=88)
create_ichimoku(dataframe,
conversion_line_period=9,
displacement=26,
base_line_periods=26,
laggin_span=52)
create_ichimoku(dataframe,
conversion_line_period=444,
displacement=444,
base_line_periods=444,
laggin_span=444)
create_ichimoku(dataframe,
conversion_line_period=100,
displacement=88,
base_line_periods=440,
laggin_span=440)
create_ichimoku(dataframe,
conversion_line_period=40,
displacement=88,
base_line_periods=176,
laggin_span=176)
dataframe['ichimoku_ok'] = (
(dataframe['kijun_sen_355_5m'] >= dataframe['tenkan_sen_355_5m']) &
(dataframe['senkou_a_100'] > dataframe['senkou_b_100']) &
(dataframe['senkou_a_20'] > dataframe['senkou_b_20']) &
(dataframe['kijun_sen_20'] > dataframe['tenkan_sen_444']) &
(dataframe['senkou_a_9'] > dataframe['senkou_a_20']) &
(dataframe['tenkan_sen_20'] >= dataframe['kijun_sen_20']) &
(dataframe['tenkan_sen_9'] >= dataframe['tenkan_sen_20']) &
(dataframe['tenkan_sen_9'] >= dataframe['kijun_sen_9'])
).astype('int') * 4
dataframe['trending_over'] = (
(dataframe['senkou_b_444'] > dataframe['close'])).astype('int') * 1
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:
# The indicators for the 1h informative timeframe
informative_1h = self.informative_1h_indicators(dataframe, metadata)
dataframe = merge_informative_pair(dataframe, informative_1h, self.timeframe, self.inf_1h, ffill=True)
# The indicators for the normal (5m) timeframe
dataframe = self.normal_tf_indicators(dataframe, metadata)
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:
# 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'] = ti.RMI(dataframe, length=21, mom=5)
dataframe['rmi-fast'] = ti.RMI(dataframe, length=8, mom=4)
# MA Streak
dataframe['mac'] = self.mac(dataframe, 20, 50)
dataframe['streak'] = self.ma_streak(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 = self.pcc(dataframe, period=20, mult=2)
dataframe['pcc-lowerband'] = pcc.lowerband
# Momentum Pinball
dataframe['roc'] = ta.ROC(dataframe, timeperiod=1)
dataframe['mp'] = ta.RSI(dataframe['roc'], timeperiod=3)
# 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 indicators for current pair on inf_timeframe
informative = self.dp.get_pair_dataframe(pair=metadata['pair'],
timeframe=self.inf_timeframe)
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:
# # 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 slow_tf_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
dataframe4h = self.dp.get_pair_dataframe(pair=metadata['pair'],
timeframe="4h")
dataframe4h['hma105'] = ftt.hull_moving_average(dataframe4h, 105)
dataframe = merge_informative_pair(dataframe,
dataframe4h,
self.timeframe,
"4h",
ffill=True)
dataframe['hma88'] = ftt.hull_moving_average(dataframe, 88)
dataframe['ema550'] = ta.EMA(dataframe, timeperiod=550)
dataframe['ema633'] = ta.EMA(dataframe, timeperiod=633)
create_ichimoku(dataframe,
conversion_line_period=20,
displacement=88,
base_line_periods=88,
laggin_span=88)
create_ichimoku(dataframe,
conversion_line_period=9,
displacement=26,
base_line_periods=26,
laggin_span=52)
create_ichimoku(dataframe,
conversion_line_period=1776,
displacement=880,
base_line_periods=880,
laggin_span=880)
dataframe['ichimoku_ok'] = (
(dataframe['kijun_sen_1776'] > dataframe['tenkan_sen_1776']) &
(dataframe['senkou_a_20'] > dataframe['senkou_b_20']) &
(dataframe['senkou_a_9'] > dataframe['senkou_a_20']) &
(dataframe['tenkan_sen_20'] >= dataframe['kijun_sen_20']) &
(dataframe['tenkan_sen_9'] >= dataframe['tenkan_sen_20']) &
(dataframe['tenkan_sen_9'] >= dataframe['kijun_sen_9']) &
(dataframe['hma105_4h'] < dataframe['kijun_sen_20']) &
(dataframe['hma88'] > dataframe['hma105_4h']) &
(dataframe['hma88'] < dataframe['tenkan_sen_9']) &
(dataframe['close'] > dataframe['open'].shift(1))
).astype('int') * 4
dataframe['trending_over'] = (
(dataframe['hma105_4h'] > dataframe['close'])).astype('int') * 1
dataframe.loc[(dataframe['ichimoku_ok'] > 0), 'trending'] = 3
dataframe.loc[(dataframe['trending_over'] > 0), 'trending'] = 0
dataframe['trending'].fillna(method='ffill', inplace=True)
return dataframe
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
informative = self.dp.get_pair_dataframe(pair=metadata['pair'],
timeframe=self.inf_timeframe)
informative['30d-low'] = informative['close'].rolling(30).min()
informative['30d-high'] = informative['close'].rolling(30).max()
dataframe = merge_informative_pair(dataframe,
informative,
self.timeframe,
self.inf_timeframe,
ffill=True)
return dataframe
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
# Strategy timeframe indicators for current pair.
dataframe['rsi'] = 14
# Informative pairs are available in this method.
dataframe['rsi_less'] = dataframe['rsi'] < dataframe['rsi_1h']
# Mixing manual informative pairs with decorators.
informative = self.dp.get_pair_dataframe('NEO/USDT', '5m')
informative['rsi'] = 14
dataframe = merge_informative_pair(dataframe,
informative,
self.timeframe,
'5m',
ffill=True)
return dataframe
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
if self.config['runmode'].value in ('backtest', 'hyperopt'):
assert (timeframe_to_minutes(self.timeframe) <=
5), "Backtest this strategy in a timeframe of 5m or less."
assert self.dp, "DataProvider is required for multiple timeframes."
informative = self.get_informative_indicators(dataframe, metadata)
dataframe = merge_informative_pair(dataframe,
informative,
self.timeframe,
self.informative_timeframe,
ffill=True)
dataframe = self.get_main_indicators(dataframe, metadata)
return dataframe
def merge_informative(self, informative: DataFrame,
dataframe: DataFrame) -> DataFrame:
dataframe = merge_informative_pair(dataframe,
informative,
self.timeframe,
self.informative_timeframe,
ffill=True)
# don't overwrite the base dataframe's HLCV information
skip_columns = [
(s + "_" + self.informative_timeframe)
for s in ['date', 'open', 'high', 'low', 'close', 'volume']
]
dataframe.rename(columns=lambda s: s.replace(
"_{}".format(self.informative_timeframe), "")
if (not s in skip_columns) else s,
inplace=True)
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:
if not metadata['pair'] in self.custom_trade_info:
self.custom_trade_info[metadata['pair']] = {}
if self.timeframe == self.informative_timeframe:
dataframe = self.coock_indicators(dataframe, metadata)
else:
assert self.dp, "DataProvider is required for multiple timeframes."
informative = self.dp.get_pair_dataframe(
pair=metadata['pair'], timeframe=self.informative_timeframe)
informative = self.coock_indicators(informative.copy(), metadata)
dataframe = merge_informative_pair(dataframe,
informative,
self.timeframe,
self.informative_timeframe,
ffill=True)
# don't overwrite the base dataframe's OHLCV information
skip_columns = [
(s + "_" + self.informative_timeframe)
for s in ['date', 'open', 'high', 'low', 'close', 'volume']
]
dataframe.rename(columns=lambda s: s.replace(
"_{}".format(self.informative_timeframe), "")
if (not s in skip_columns) else s,
inplace=True)
informative_btc = self.dp.get_pair_dataframe(
pair='BTC/USDT', timeframe=self.informative_timeframe)
informative_btc['EWO_BTC'] = EWO(informative_btc, 10, 40)
informative_btc['ema32_btc'] = fta.EMA(informative_btc, period=32)
informative_btc['btc_open'] = informative_btc['open']
dataframe = merge_informative_pair(dataframe,
informative_btc,
self.timeframe,
self.informative_timeframe,
ffill=True)
# don't overwrite the base dataframe's OHLCV information
skip_columns = [
(s + "_" + self.informative_timeframe)
for s in ['date', 'open', 'high', 'low', 'close', 'volume']
]
dataframe.rename(columns=lambda s: s.replace(
"_{}".format(self.informative_timeframe), "")
if (not s in skip_columns) else s,
inplace=True)
dataframe['4h_high'] = dataframe['close'].rolling(48).max()
dataframe['8h_high'] = dataframe['close'].rolling(96).max()
if self.dp.runmode.value in ('backtest', 'hyperopt'):
self.custom_trade_info[metadata['pair']]['EWO_BUY'] = dataframe[[
'date', 'EWO_BUY'
]].copy().set_index('date')
self.custom_trade_info[metadata['pair']]['EWO_SELL'] = dataframe[[
'date', 'EWO_SELL'
]].copy().set_index('date')
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'])
# Base timeframe indicators
dataframe['rmi-slow'] = cta.RMI(dataframe, length=21, mom=5)
dataframe['rmi-fast'] = cta.RMI(dataframe, length=8, mom=4)
# Indicators for ROI and Custom Stoploss
dataframe['atr'] = ta.ATR(dataframe, timeperiod=24)
dataframe['roc'] = ta.ROC(dataframe, timeperiod=9)
# Momentum Pinball: https://www.tradingview.com/script/fBpVB1ez-Momentum-Pinball-Indicator/
dataframe['roc-mp'] = ta.ROC(dataframe, timeperiod=6)
dataframe['mp'] = ta.RSI(dataframe['roc-mp'], timeperiod=6)
# Trends, Peaks and Crosses
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)
# Base pair informative timeframe indicators
informative = self.dp.get_pair_dataframe(pair=metadata['pair'],
timeframe=self.inf_timeframe)
# 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)
# Other stake specific informative indicators
# e.g if stake is BTC and current coin is XLM (pair: XLM/BTC)
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}"
# Informative COIN/FIAT e.g. XLM/USD - Base Timeframe
coin_fiat_tf = self.dp.get_pair_dataframe(pair=coin_fiat,
timeframe=self.timeframe)
dataframe[f"{fiat}_rmi"] = cta.RMI(coin_fiat_tf, length=21, mom=5)
# Informative STAKE/FIAT e.g. BTC/USD - Base Timeframe
stake_fiat_tf = self.dp.get_pair_dataframe(
pair=stake_fiat, timeframe=self.timeframe)
dataframe[f"{stake}_rmi"] = cta.RMI(stake_fiat_tf,
length=21,
mom=5)
# Informative STAKE/FIAT e.g. BTC/USD - Informative Timeframe
stake_fiat_inf_tf = self.dp.get_pair_dataframe(
pair=stake_fiat, timeframe=self.inf_timeframe)
stake_fiat_inf_tf[f"{stake}_rmi"] = cta.RMI(stake_fiat_inf_tf,
length=48,
mom=5)
dataframe = merge_informative_pair(dataframe,
stake_fiat_inf_tf,
self.timeframe,
self.inf_timeframe,
ffill=True)
# Informatives for BTC/STAKE if not in whitelist
else:
pairs = self.dp.current_whitelist()
btc_stake = f"BTC/{self.config['stake_currency']}"
if not btc_stake in pairs:
# BTC/STAKE - Base Timeframe
btc_stake_tf = self.dp.get_pair_dataframe(
pair=btc_stake, timeframe=self.timeframe)
dataframe['BTC_rmi'] = cta.RMI(btc_stake_tf, length=14, mom=3)
# BTC/STAKE - Informative Timeframe
btc_stake_inf_tf = self.dp.get_pair_dataframe(
pair=btc_stake, timeframe=self.inf_timeframe)
btc_stake_inf_tf['BTC_rmi'] = cta.RMI(btc_stake_inf_tf,
length=48,
mom=5)
dataframe = merge_informative_pair(dataframe,
btc_stake_inf_tf,
self.timeframe,
self.inf_timeframe,
ffill=True)
# Slam some indicators into the trade_info dict so we can dynamic roi and custom stoploss in backtest
if self.dp.runmode.value in ('backtest', 'hyperopt'):
self.custom_trade_info[metadata['pair']]['roc'] = dataframe[[
'date', 'roc'
]].copy().set_index('date')
self.custom_trade_info[metadata['pair']]['atr'] = dataframe[[
'date', 'atr'
]].copy().set_index('date')
self.custom_trade_info[metadata['pair']]['rmi-slow'] = dataframe[[
'date', 'rmi-slow'
]].copy().set_index('date')
self.custom_trade_info[
metadata['pair']]['rmi-up-trend'] = dataframe[[
'date', 'rmi-up-trend'
]].copy().set_index('date')
return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
buy_params = self.get_pair_params(metadata['pair'], 'buy')
sell_params = self.get_pair_params(metadata['pair'], 'sell')
self.custom_trade_info[metadata['pair']] = self.populate_trades(metadata['pair'])
# Base timeframe indicators
dataframe['rmi-slow'] = cta.RMI(dataframe, length=21, mom=5)
dataframe['rmi-fast'] = cta.RMI(dataframe, length=8, mom=4)
# Momentum Pinball: https://www.tradingview.com/script/fBpVB1ez-Momentum-Pinball-Indicator/
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()
# Fibonacci Retracements
dataframe['fib-ret'] = cta.fib_ret(dataframe)
# PMAX
# matype = ema, src = hl2
# pmax is really slow, only enable it if its actually being used.
if buy_params['base-pmax-enable'] == True or sell_params['sell-pmax-enable'] == True:
atr, ma, pmax, pm_loc = cta.PMAX(dataframe, period=10, multiplier=3, length=12, MAtype=1, src=2)
dataframe['pmax-trend'] = pm_loc
else:
dataframe['pmax-trend'] = False
# Other stake specific informative indicators
# e.g if stake is BTC and current coin is XLM (pair: XLM/BTC)
# 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}"
# Informative COIN/FIAT e.g. XLM/USD
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)
# Informative STAKE/FIAT e.g. BTC/USD
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}"] = cta.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']
informative['fib-ret'] = cta.fib_ret(informative)
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 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
