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) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
"""
dataframe['sar'] = ta.SAR(dataframe)
dataframe['adx'] = ta.ADX(dataframe)
stoch = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch['fastd']
dataframe['fastk'] = stoch['fastk']
dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2,
nbdevdn=2)['lowerband']
dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
dataframe['mfi'] = ta.MFI(dataframe)
dataframe['cci'] = ta.CCI(dataframe)
dataframe['rsi'] = ta.RSI(dataframe)
dataframe['mom'] = ta.MOM(dataframe)
dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
dataframe['ao'] = awesome_oscillator(dataframe)
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
return dataframe
def populate_indicators(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 do_populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
"""
Adds multiple TA indicators to MoniGoMani's DataFrame per pair.
Should be called with 'informative_pair' (1h candles) during backtesting/hyperopting with TimeFrame-Zoom!
Performance Note: For the best performance be frugal on the number of indicators you are using.
Only add in indicators that you are using in your weighted signal configuration for MoniGoMani,
otherwise you will waste your memory and CPU usage.
:param dataframe: (DataFrame) DataFrame with data from the exchange
:param metadata: (dict) Additional information, like the currently traded pair
:return DataFrame: DataFrame for MoniGoMani with all mandatory indicator data populated
"""
# Momentum Indicators (timeperiod is expressed in candles)
# -------------------
# Parabolic SAR
dataframe['sar'] = ta.SAR(dataframe)
# Stochastic Slow
stoch = ta.STOCH(dataframe)
dataframe['slowk'] = stoch['slowk']
# 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)
# MFI - Money Flow Index (Under bought / Over sold & Over bought / Under sold / volume Indicator)
dataframe['mfi'] = ta.MFI(dataframe)
# Overlap Studies
# ---------------
# Bollinger Bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=20,
stds=2)
dataframe['bb_middleband'] = bollinger['mid']
# SMA's & EMA's are trend following tools (Should not be used when line goes sideways)
# SMA - Simple Moving Average (Moves slower compared to EMA, price trend over X periods)
dataframe['sma9'] = ta.SMA(dataframe, timeperiod=9)
dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50)
dataframe['sma200'] = ta.SMA(dataframe, timeperiod=200)
# TEMA - Triple Exponential Moving Average
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
# Volume Indicators
# -----------------
# Rolling VWAP - Volume Weighted Average Price
dataframe['rolling_vwap'] = qtpylib.rolling_vwap(dataframe)
return dataframe
def analyze(self,
historical_data,
signal=['sar'],
hot_thresh=None,
cold_thresh=None):
"""Performs a macd analysis on the historical data
Args:
historical_data (list): A matrix of historical OHCLV data.
signal (list, optional): Defaults to macd. The indicator line to check hot/cold
against.
hot_thresh (float, optional): Defaults to None. The threshold at which this might be
good to purchase.
cold_thresh (float, optional): Defaults to None. The threshold at which this might be
good to sell.
Returns:
pandas.DataFrame: A dataframe containing the indicators and hot/cold values.
"""
dataframe = self.convert_to_dataframe(historical_data)
sar_values = abstract.SAR(dataframe).iloc[:]
sar_values.dropna(how='all', inplace=True)
if sar_values[signal[0]].shape[0]:
sar_values['is_hot'] = sar_values[signal[0]] > hot_thresh
sar_values['is_cold'] = sar_values[signal[0]] < cold_thresh
return sar_values
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.
"""
# Stoch
stoch = ta.STOCH(dataframe)
dataframe['slowk'] = stoch['slowk']
# 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)
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=20,
stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
# SAR Parabol
dataframe['sar'] = ta.SAR(dataframe)
# Hammer: values [0, 100]
dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
return dataframe
def populate_indicators_and_buy_signal(dataframe):
dataframe['ema'] = ta.EMA(dataframe, timeperiod=33)
dataframe['sar'] = ta.SAR(dataframe, 0.02, 0.22)
dataframe['adx'] = ta.ADX(dataframe)
prev_sar = dataframe['sar'].shift(1)
prev_close = dataframe['close'].shift(1)
prev_sar2 = dataframe['sar'].shift(2)
prev_close2 = dataframe['close'].shift(2)
# wait for stable turn from bearish to bullish market
dataframe.loc[(dataframe['close'] > dataframe['sar']) &
(prev_close > prev_sar) & (prev_close2 < prev_sar2),
'swap'] = 1
# consider prices above ema to be in upswing
dataframe.loc[dataframe['ema'] <= dataframe['close'], 'upswing'] = 1
dataframe.loc[(dataframe['upswing'] == 1) & (dataframe['swap'] == 1) & (
dataframe['adx'] > 25), # adx over 25 tells there's enough momentum
'buy'] = 1
dataframe.loc[dataframe['buy'] == 1, 'buy_price'] = dataframe['close']
return dataframe, info_msg % (dataframe.iloc[-1]['ema'],
dataframe.iloc[-1]['sar'],
dataframe.iloc[-1]['adx'])
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['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 SAR(ohlcv, kw):
""" :return Parabolic SAR (sar) """
params = {'acceleration': 0, 'maximum': 0}
acceleration, maximum = _get_params(kw, params, ['acceleration', 'maximum'])
result = talib.SAR(ohlcv, acceleration, maximum)
return {
'sar': result,
}
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:
# Set Up Bollinger Bands
upper_bb1, mid_bb1, lower_bb1 = ta.BBANDS(dataframe['close'],
timeperiod=36)
upper_bb2, mid_bb2, lower_bb2 = ta.BBANDS(
qtpylib.typical_price(dataframe), timeperiod=12)
# Only putting some bands into dataframe as the others are not used elsewhere in the strategy
dataframe['lower-bb1'] = lower_bb1
dataframe['lower-bb2'] = lower_bb2
dataframe['mid-bb2'] = mid_bb2
dataframe['bb1-delta'] = (mid_bb1 - dataframe['lower-bb1']).abs()
dataframe['closedelta'] = (dataframe['close'] -
dataframe['close'].shift()).abs()
dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs()
# Additional indicators
dataframe['ema_fast'] = ta.EMA(dataframe['close'], timeperiod=6)
dataframe['ema_slow'] = ta.EMA(dataframe['close'], timeperiod=48)
dataframe['volume_mean_slow'] = dataframe['volume'].rolling(
window=24).mean()
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
# Inverse Fisher transform on RSI: values [-1.0, 1.0] (https://goo.gl/2JGGoy)
rsi = 0.1 * (dataframe['rsi'] - 50)
dataframe['fisher-rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
# Informative Pair Indicators
coin, stake = metadata['pair'].split('/')
fiat = self.fiat
stake_fiat = f"{stake}/{self.fiat}"
coin_fiat = f"{coin}/{self.fiat}"
coin_fiat_inf = self.dp.get_pair_dataframe(pair=f"{coin}/{fiat}",
timeframe=self.timeframe)
dataframe['coin-fiat-adx'] = ta.ADX(coin_fiat_inf, timeperiod=21)
coin_aroon = ta.AROON(coin_fiat_inf, timeperiod=25)
dataframe['coin-fiat-aroon-down'] = coin_aroon['aroondown']
dataframe['coin-fiat-aroon-up'] = coin_aroon['aroonup']
stake_fiat_inf = self.dp.get_pair_dataframe(pair=f"{stake}/{fiat}",
timeframe=self.timeframe)
dataframe['stake-fiat-adx'] = ta.ADX(stake_fiat_inf, timeperiod=21)
stake_aroon = ta.AROON(stake_fiat_inf, timeperiod=25)
dataframe['stake-fiat-aroon-down'] = stake_aroon['aroondown']
dataframe['stake-fiat-aroon-up'] = stake_aroon['aroonup']
# These indicators are used to persist a buy signal in live trading only
# They dramatically slow backtesting down
if self.config['runmode'].value in ('live', 'dry_run'):
dataframe['sar'] = ta.SAR(dataframe)
return dataframe
def populate_indicators(dataframe: DataFrame) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
"""
# Log the dataframe
dataframe.to_csv("dataframe.csv", sep='\t')
#Determine Trend
dataframe = indicators.TREND(dataframe)
#Exponential Moving Average
dataframe['ema'] = ta.EMA(dataframe, timeperiod=33)
#Parabolic SAR
dataframe['sar'] = ta.SAR(dataframe, 0.02, 0.22)
#Average Directional Movement Index
dataframe['adx'] = ta.ADX(dataframe)
#MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macds'] = macd['macdsignal']
dataframe['macdh'] = macd['macdhist']
#Relative Strength Index
dataframe['rsi'] = ta.RSI(dataframe, 14)
dataframe = indicators.RSI(dataframe)
#Absolute Price Oscillator
dataframe['apo'] = ta.APO(dataframe,
fastperiod=12,
slowperiod=26,
matype=0)
#Momentum
dataframe['mom'] = ta.MOM(dataframe, 10)
#Bollinger Bands
dataframe = indicators.BBANDS(20, dataframe, 2)
# Stochcastic
dataframe['K'] = indicators.STOK(dataframe, 14)
dataframe['D'] = indicators.STOD(dataframe, 14)
dataframe = indicators.STOCH(dataframe)
# if Counter.counter < 30:
# print(dataframe)
# print (macd)
# print (dataframe ['adx'])
# Counter.counter = Counter.counter + 1
# else:
# exit()
return dataframe
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
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(dataframe: DataFrame) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
"""
dataframe['ema'] = ta.EMA(dataframe, timeperiod=33)
dataframe['sar'] = ta.SAR(dataframe, 0.02, 0.22)
dataframe['adx'] = ta.ADX(dataframe)
return dataframe
def sar(self):
sar = tb.SAR(self.dataframe, acceleration=0, maximum=0)
if self.dataframe['open'][len(self.dataframe) - 1] > sar[len(sar) - 1]:
return "buy"
elif self.dataframe['open'][len(self.dataframe) - 1] < sar[len(sar) -
1]:
return "sell"
else:
return "neutral"
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:
dataframe['sar'] = ta.SAR(dataframe)
if self.dp.runmode.value in ('backtest', 'hyperopt'):
self.custom_info[metadata['pair']] = dataframe[[
'date', 'sar'
]].copy().set_index('date')
# all "normal" indicators:
# e.g.
# dataframe['rsi'] = ta.RSI(dataframe)
return dataframe
def SAR(self):
SAR = tb.SAR(self.dataframe, acceleration=0, maximum=0)
if (self.dataframe['open'][len(self.dataframe) - 1] >
SAR[len(SAR) - 1]):
#print("SAR: buy")
return "buy"
elif (self.dataframe['open'][len(self.dataframe) - 1] <
SAR[len(SAR) - 1]):
#print("SAR: sell")
return "sell"
else:
#print("SAR: neutral")
return "neutral"
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
Performance Note: For the best performance be frugal on the number of indicators
you are using. Let uncomment only the indicator you are using in your strategies
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
:param dataframe: Dataframe with data from the exchange
:param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
"""
# Momentum Indicators
# ------------------------------------
dataframe['rsi'] = ta.RSI(dataframe)
# # Stochastic RSI
stoch_rsi = ta.STOCHRSI(dataframe)
# dataframe['fastd_rsi'] = stoch_rsi['fastd']
dataframe['fastk_rsi'] = stoch_rsi['fastk']
# # 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)
# MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
# dataframe['macdsignal'] = macd['macdsignal']
# dataframe['macdhist'] = macd['macdhist']
# Parabolic SAR
dataframe['sar'] = ta.SAR(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']
# 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"]
# )
return dataframe
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
dataframe['ohlc4'] = ta.AVGPRICE(dataframe)
dataframe['hlc3'] = (dataframe['high'] + dataframe['low'] +
dataframe['close']) / 3
dataframe['hl2'] = (dataframe['high'] + dataframe['low']) / 2
dataframe['atr'] = ta.ATR(dataframe, timeperiod=10)
dataframe['sar'] = ta.SAR(dataframe)
dataframe = supertrend(dataframe, multiplier=1)
return dataframe
def populate_indicators(dataframe: DataFrame) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
"""
dataframe['sar'] = ta.SAR(dataframe)
dataframe['adx'] = ta.ADX(dataframe)
stoch = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch['fastd']
dataframe['fastk'] = stoch['fastk']
dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband']
dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
dataframe['mfi'] = ta.MFI(dataframe)
dataframe['cci'] = ta.CCI(dataframe)
return dataframe
def populate_indicators(self, dataframe, metadata):
stoch = ta.STOCH(dataframe)
rsi = ta.RSI(dataframe)
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe))
dataframe["slowk"] = stoch["slowk"]
dataframe["rsi"] = rsi
rsi = 0.1 * (rsi - 50)
dataframe["fisher"] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
dataframe["bb_lowerband"] = bollinger["lower"]
dataframe["sar"] = ta.SAR(dataframe)
dataframe["CDLHAMMER"] = ta.CDLHAMMER(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 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 get_ticker_dataframe(pair: str) -> DataFrame:
"""
Analyses the trend for the given pair
:param pair: pair as str in format BTC_ETH or BTC-ETH
:return: DataFrame
"""
minimum_date = arrow.now() - timedelta(hours=6)
url = 'https://bittrex.com/Api/v2.0/pub/market/GetTicks'
headers = {
'User-Agent':
'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.36',
}
params = {
'marketName': pair.replace('_', '-'),
'tickInterval': 'OneMin',
'_': minimum_date.timestamp * 1000
}
data = requests.get(url, params=params, headers=headers).json()
if not data['success']:
raise RuntimeError('BITTREX: {}'.format(data['message']))
data = [{
'close': t['C'],
'volume': t['V'],
'open': t['O'],
'high': t['H'],
'low': t['L'],
'date': t['T'],
} for t in sorted(data['result'], key=lambda k: k['T'])
if arrow.get(t['T']) > minimum_date]
dataframe = DataFrame(json_normalize(data))
dataframe['close_30_ema'] = ta.EMA(dataframe, timeperiod=30)
dataframe['close_90_ema'] = ta.EMA(dataframe, timeperiod=90)
dataframe['sar'] = ta.SAR(dataframe, 0.02, 0.2)
# calculate StochRSI
stochrsi = ta.STOCHRSI(dataframe)
dataframe['stochrsi'] = stochrsi['fastd'] # values between 0-100, not 0-1
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macds'] = macd['macdsignal']
dataframe['macdh'] = macd['macdhist']
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
# ------------------------------------
dataframe['adx'] = ta.ADX(dataframe)
dataframe['sar'] = ta.SAR(dataframe)
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
dataframe['mfi'] = ta.MFI(dataframe)
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
# Bollinger Bands 1 STD
bollinger1 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=20,
stds=1)
dataframe['bb_lowerband1'] = bollinger1['lower']
# dataframe['bb_middleband1'] = bollinger1['mid']
# dataframe['bb_upperband1'] = bollinger1['upper']
# bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
# dataframe['bb_lowerband2'] = bollinger2['lower']
# dataframe['bb_middleband2'] = bollinger2['mid']
# dataframe['bb_upperband2'] = bollinger2['upper']
# bollinger3 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=3)
# dataframe['bb_lowerband3'] = bollinger3['lower']
# dataframe['bb_middleband3'] = bollinger3['mid']
# dataframe['bb_upperband3'] = bollinger3['upper']
# Bollinger Bands 4 STD
bollinger4 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=20,
stds=4)
dataframe['bb_lowerband4'] = bollinger4['lower']
# dataframe['bb_middleband4'] = bollinger4['mid']
# dataframe['bb_upperband4'] = bollinger4['upper']
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
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.
"""
# MFI
dataframe['mfi'] = ta.MFI(dataframe)
# Stoch fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
# 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)
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=20,
stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
# EMA - Exponential Moving Average
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)
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(dataframe: DataFrame) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
"""
dataframe['close_30_ema'] = ta.EMA(dataframe, timeperiod=30)
dataframe['close_90_ema'] = ta.EMA(dataframe, timeperiod=90)
dataframe['sar'] = ta.SAR(dataframe, 0.02, 0.2)
# calculate StochRSI
stochrsi = ta.STOCHRSI(dataframe)
dataframe['stochrsi'] = stochrsi['fastd'] # values between 0-100, not 0-1
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macds'] = macd['macdsignal']
dataframe['macdh'] = macd['macdhist']
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
