def __init__(self): """ Class Constructor. """ tableName = "Indicator_ADI" self._insertQuery = "insert or replace into %s (Date, ADI, ADI_SUM, ADI_ROC, ADI_EMA) values (?,?,?,?,?)" % ( tableName) self._selectQuery = "SELECT Date, SUM(CASE WHEN Close > Open THEN 1 ELSE 0 END) - SUM(CASE WHEN Close < Open THEN 1 ELSE 0 END) as ADI FROM Equities group by Date" connection = sqlite3.connect(pyswing.database.pySwingDatabase) self._indicatorDataFrame = read_sql_query(self._selectQuery, connection, 'Date') self._indicatorDataFrame['ADI'] = self._indicatorDataFrame[ 'ADI'].astype(float) self._indicatorDataFrame['ADI_SUM'] = self._indicatorDataFrame[ 'ADI'].cumsum() self._indicatorDataFrame['ADI_ROC'] = abstract.ROC( self._indicatorDataFrame, timeperiod=5, price='ADI_SUM') self._indicatorDataFrame['ADI_EMA'] = abstract.EMA( self._indicatorDataFrame, timeperiod=5, price='ADI') self._tableName = "Indicator_ADI"
def __init__(self, equityDataFrame, tickerCode): """ Create the Average Directional-specific logic. Everything else lives in the Indicator Base Class. :param equityDataFrame: A Pandas DataFrame from the Equity Class. :param tickerCode: Ticker Code (String). """ tableName = "Indicator_ADX" tickerCode = tickerCode insertQuery = "insert or replace into %s (Date, Code, ADX, ADX_ROC) values (?,?,?,?)" % ( tableName) # Create a new DataFrame with just Date and Code indicatorDataFrame = equityDataFrame.ix[:, 'Code':] # Stick the Indicator Values into the new DataFrame indicatorDataFrame['ADX'] = abstract.ADX( equityDataFrame, timeperiod=14, prices=['High', 'Low', 'Close']) indicatorDataFrame['ADX_ROC'] = abstract.ROC(indicatorDataFrame, timeperiod=5, price='ADX') Indicator.__init__(self, tableName, tickerCode, insertQuery, equityDataFrame, indicatorDataFrame)
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: # 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 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: # RSI dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_84'] = ta.RSI(dataframe, timeperiod=84) dataframe['rsi_112'] = ta.RSI(dataframe, timeperiod=112) # Bollinger bands bollinger1 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=17, stds=1) dataframe['bb_lowerband'] = bollinger1['lower'] dataframe['bb_middleband'] = bollinger1['mid'] dataframe['bb_upperband'] = bollinger1['upper'] # Close delta dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs() # Dip Protection dataframe['tpct_change_0'] = top_percent_change(dataframe, 0) dataframe['tpct_change_1'] = top_percent_change(dataframe, 1) dataframe['tpct_change_2'] = top_percent_change(dataframe, 2) dataframe['tpct_change_4'] = top_percent_change(dataframe, 4) dataframe['tpct_change_5'] = top_percent_change(dataframe, 5) dataframe['tpct_change_9'] = top_percent_change(dataframe, 9) # SMA dataframe['sma_50'] = ta.SMA(dataframe['close'], timeperiod=50) dataframe['sma_200'] = ta.SMA(dataframe['close'], timeperiod=200) # CTI dataframe['cti'] = pta.cti(dataframe["close"], length=20) # ADX dataframe['adx'] = ta.ADX(dataframe) # %R dataframe['r_14'] = williams_r(dataframe, period=14) dataframe['r_96'] = williams_r(dataframe, period=96) # MAMA / FAMA dataframe['hl2'] = (dataframe['high'] + dataframe['low']) / 2 dataframe['mama'], dataframe['fama'] = ta.MAMA(dataframe['hl2'], 0.5, 0.05) dataframe['mama_diff'] = ((dataframe['mama'] - dataframe['fama']) / dataframe['hl2']) # CRSI (3, 2, 100) crsi_closechange = dataframe['close'] / dataframe['close'].shift(1) crsi_updown = np.where(crsi_closechange.gt(1), 1.0, np.where(crsi_closechange.lt(1), -1.0, 0.0)) dataframe['crsi'] = (ta.RSI(dataframe['close'], timeperiod=3) + ta.RSI( crsi_updown, timeperiod=2) + ta.ROC(dataframe['close'], 100)) / 3 return dataframe
def apply_indicators(df: pd.DataFrame): # ADX df['adx'] = ta.ADX(df) # EMA df['ema_5'] = ta.EMA(df, 5) df['ema_10'] = ta.EMA(df, 10) df['ema_20'] = ta.EMA(df, 20) df['ema_50'] = ta.EMA(df, 50) df['ema_100'] = ta.EMA(df, 100) df['ema_200'] = ta.EMA(df, 200) # MACD macd = ta.MACD(df) df['macd'] = macd['macd'] df['macdsignal'] = macd['macdsignal'] df['macdhist'] = macd['macdhist'] # inverse Fisher rsi/ RSI df['rsi'] = ta.RSI(df) rsi = 0.1 - (df['rsi'] - 50) df['i_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1) # Stoch fast stoch_fast = ta.STOCHF(df) df['fastd'] = stoch_fast['fastd'] df['fastk'] = stoch_fast['fastk'] # Stock slow stoch_slow = ta.STOCH(df) df['slowd'] = stoch_slow['slowd'] df['slowk'] = stoch_slow['slowk'] # Bollinger bands bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(df), window=20, stds=2) df['bb_lowerband'] = bollinger['lower'] df['bb_middleband'] = bollinger['mid'] df['bb_upperband'] = bollinger['upper'] # ROC df['roc'] = ta.ROC(df, 10) # CCI df['cci'] = ta.CCI(df, 14) # on balance volume df['obv'] = ta.OBV(df) # Average True Range df['atr'] = ta.ATR(df, 14) df = ichimoku(df) return df
def technical_index(self): df = self.max_min_price() df2 = self.institutional_investors() df['RSI'] = abstract.RSI(df) / 100 df['CMO'] =(abstract.CMO(df)+100) / (2 *100) df['MACD'] =(abstract.MACD(df)['macd']+abstract.MACD(df)['macd'].max()) / (2 *abstract.MACD(df)['macd'].max()) df['WILLR'] =(abstract.WILLR(df)+100) / (2 *100) df['WMA'] =abstract.WMA(df) / abstract.WMA(df).max() df['PPO'] =(abstract.PPO(df)+abstract.PPO(df).max()) / (2 *abstract.PPO(df).max()) df['EMA'] =abstract.EMA(df) / abstract.EMA(df).max() df['ROC'] =(abstract.ROC(df)+abstract.ROC(df).max()) / (2 *abstract.ROC(df).max()) df['SMA'] =abstract.SMA(df) / abstract.SMA(df).max() df['TEMA'] =abstract.TEMA(df) / abstract.TEMA(df).max() df['CCI'] =(abstract.CCI(df)+abstract.CCI(df).max()) / (2 *abstract.CCI(df).max()) df['investment_trust'] = (df2['investment_trust'] + df2['investment_trust'].max()) / (2*df2['investment_trust'].max()) df['foreign_investor'] = (df2['foreign_investor'] + df2['foreign_investor'].max()) / (2*df2['foreign_investor'].max()) df = df.drop(columns=['volume', 'open', 'high', 'low', 'close', 'close_max', 'close_min']) df = df.dropna() return df
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: dataframe['color'] = dataframe['close'] > dataframe['open'] macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] dataframe['rsi'] = ta.RSI(dataframe, timeperiod=7) dataframe['roc'] = ta.ROC(dataframe, timeperiod=6) dataframe['primed'] = np.where(dataframe['color'].rolling(3).sum() == 3,1,0) dataframe['in-the-mood'] = dataframe['rsi'] > dataframe['rsi'].rolling(12).mean() dataframe['moist'] = qtpylib.crossed_above(dataframe['macd'], dataframe['macdsignal']) dataframe['throbbing'] = dataframe['roc'] > dataframe['roc'].rolling(12).mean() dataframe['ready-to-go'] = np.where(dataframe['close'] > dataframe['open'].rolling(12).mean(), 1,0) 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: Raw data from the exchange and parsed by parse_ticker_dataframe() :param metadata: Additional information, like the currently traded pair :return: a Dataframe with all mandatory indicators for the strategies """ length = 20 dataframe['roc'] = ta.ROC(dataframe, timeperiod=length) dataframe['max'] = ta.MAX(dataframe, timeperiod=length) dataframe['min'] = ta.MIN(dataframe, timeperiod=length) 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 TA_processing(dataframe): bias(dataframe, days=[3, 6, 10, 25]) moving_average(dataframe, days=[5, 10, 20]) dataframe['ROC'] = abstract.ROC(dataframe, timeperiod=10) dataframe['MACD'] = abstract.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9)['macd'] dataframe['MACD_signal'] = abstract.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9)['macdsignal'] dataframe['UBBANDS'] = abstract.BBANDS(dataframe, timeperiod=20, nbdevup=2, nbdevdn=2, matype=0)['upperband'] dataframe['MBBANDS'] = abstract.BBANDS(dataframe, timeperiod=20, nbdevup=2, nbdevdn=2, matype=0)['middleband'] dataframe['LBBANDS'] = abstract.BBANDS(dataframe, timeperiod=20, nbdevup=2, nbdevdn=2, matype=0)['lowerband'] dataframe['%K'] = abstract.STOCH(dataframe, fastk_period=9)['slowk']/100 dataframe['%D'] = abstract.STOCH(dataframe, fastk_period=9)['slowd']/100 dataframe['W%R'] = abstract.WILLR(dataframe, timeperiod=14)/100 dataframe['RSI9'] = abstract.RSI(dataframe, timeperiod = 9)/100 dataframe['RSI14'] = abstract.RSI(dataframe, timeperiod = 14)/100 dataframe['CCI'] = abstract.CCI(dataframe, timeperiod=14)/100 counter_daily_potential(dataframe) dataframe['MOM'] = abstract.MOM(dataframe, timeperiod=10) dataframe['DX'] = abstract.DX(dataframe, timeperiod=14)/100 psy_line(dataframe) volumn_ratio(dataframe, d=26) on_balance_volume(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: # 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']) # Indicators for ROI and Custom Stoploss dataframe['atr'] = ta.ATR(dataframe, timeperiod=24) dataframe['roc'] = ta.ROC(dataframe, timeperiod=9) dataframe['rmi-slow'] = cta.RMI(dataframe, length=21, mom=5) # Trends, Peaks and Crosses dataframe['rmi-up'] = 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) # strategy BinHV45 mid, lower = cta.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() # strategy ClucMay72018 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() # 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 ROC(self): #12 roc = abstract.ROC(self.company_stock, timeperiod=12) self.company_stock['ROC'] = roc
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Momentum Indicators # ------------------------------------ # ADX dataframe['adx'] = ta.ADX(dataframe) # Plus Directional Indicator / Movement dataframe['plus_dm'] = ta.PLUS_DM(dataframe) dataframe['plus_di'] = ta.PLUS_DI(dataframe) # # Minus Directional Indicator / Movement dataframe['minus_dm'] = ta.MINUS_DM(dataframe) dataframe['minus_di'] = ta.MINUS_DI(dataframe) # Aroon, Aroon Oscillator aroon = ta.AROON(dataframe) dataframe['aroonup'] = aroon['aroonup'] dataframe['aroondown'] = aroon['aroondown'] dataframe['aroonosc'] = ta.AROONOSC(dataframe) # Awesome Oscillator dataframe['ao'] = qtpylib.awesome_oscillator(dataframe) # # Keltner Channel # keltner = qtpylib.keltner_channel(dataframe) # dataframe["kc_upperband"] = keltner["upper"] # dataframe["kc_lowerband"] = keltner["lower"] # dataframe["kc_middleband"] = keltner["mid"] # dataframe["kc_percent"] = ( # (dataframe["close"] - dataframe["kc_lowerband"]) / # (dataframe["kc_upperband"] - dataframe["kc_lowerband"]) # ) # dataframe["kc_width"] = ( # (dataframe["kc_upperband"] - dataframe["kc_lowerband"]) / dataframe["kc_middleband"] # ) # Ultimate Oscillator dataframe['uo'] = ta.ULTOSC(dataframe) # Commodity Channel Index: values [Oversold:-100, Overbought:100] dataframe['cci'] = ta.CCI(dataframe) # RSI dataframe['rsi'] = ta.RSI(dataframe) # Inverse Fisher transform on RSI: values [-1.0, 1.0] (https://goo.gl/2JGGoy) rsi = 0.1 * (dataframe['rsi'] - 50) dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1) # Inverse Fisher transform on RSI normalized: values [0.0, 100.0] (https://goo.gl/2JGGoy) dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1) # Stochastic Slow stoch = ta.STOCH(dataframe) dataframe['slowd'] = stoch['slowd'] dataframe['slowk'] = stoch['slowk'] # Stochastic Fast stoch_fast = ta.STOCHF(dataframe) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] # Stochastic RSI stoch_rsi = ta.STOCHRSI(dataframe) dataframe['fastd_rsi'] = stoch_rsi['fastd'] dataframe['fastk_rsi'] = stoch_rsi['fastk'] # MACD macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] # MFI dataframe['mfi'] = ta.MFI(dataframe) # # ROC dataframe['roc'] = ta.ROC(dataframe) # Overlap Studies # ------------------------------------ # # Bollinger Bands # bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) # dataframe['bb_lowerband'] = bollinger['lower'] # dataframe['bb_middleband'] = bollinger['mid'] # dataframe['bb_upperband'] = bollinger['upper'] # dataframe["bb_percent"] = ( # (dataframe["close"] - dataframe["bb_lowerband"]) / # (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) # ) # dataframe["bb_width"] = ( # (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"] # ) # # Bollinger Bands - Weighted (EMA based instead of SMA) # weighted_bollinger = qtpylib.weighted_bollinger_bands( # qtpylib.typical_price(dataframe), window=20, stds=2 # ) # dataframe["wbb_upperband"] = weighted_bollinger["upper"] # dataframe["wbb_lowerband"] = weighted_bollinger["lower"] # dataframe["wbb_middleband"] = weighted_bollinger["mid"] # dataframe["wbb_percent"] = ( # (dataframe["close"] - dataframe["wbb_lowerband"]) / # (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"]) # ) # dataframe["wbb_width"] = ( # (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"]) / # dataframe["wbb_middleband"] # ) # # EMA - Exponential Moving Average # dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3) # dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5) # dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10) # dataframe['ema21'] = ta.EMA(dataframe, timeperiod=21) # dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50) # dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100) # # SMA - Simple Moving Average # dataframe['sma3'] = ta.SMA(dataframe, timeperiod=3) # dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5) # dataframe['sma10'] = ta.SMA(dataframe, timeperiod=10) # dataframe['sma21'] = ta.SMA(dataframe, timeperiod=21) # dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50) # dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100) # Parabolic SAR # dataframe['sar'] = ta.SAR(dataframe) # TEMA - Triple Exponential Moving Average # dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9) # # Cycle Indicator # # ------------------------------------ # # Hilbert Transform Indicator - SineWave # hilbert = ta.HT_SINE(dataframe) # dataframe['htsine'] = hilbert['sine'] # dataframe['htleadsine'] = hilbert['leadsine'] # # Pattern Recognition - Bullish candlestick patterns # # ------------------------------------ # # Hammer: values [0, 100] # dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe) # # Inverted Hammer: values [0, 100] # dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe) # # Dragonfly Doji: values [0, 100] # dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe) # # Piercing Line: values [0, 100] # dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100] # # Morningstar: values [0, 100] # dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100] # # Three White Soldiers: values [0, 100] # dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100] # # Pattern Recognition - Bearish candlestick patterns # # ------------------------------------ # # Hanging Man: values [0, 100] # dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe) # # Shooting Star: values [0, 100] # dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe) # # Gravestone Doji: values [0, 100] # dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe) # # Dark Cloud Cover: values [0, 100] # dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe) # # Evening Doji Star: values [0, 100] # dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe) # # Evening Star: values [0, 100] # dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe) # # Pattern Recognition - Bullish/Bearish candlestick patterns # # ------------------------------------ # # Three Line Strike: values [0, -100, 100] # dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe) # # Spinning Top: values [0, -100, 100] # dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100] # # Engulfing: values [0, -100, 100] # dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100] # # Harami: values [0, -100, 100] # dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100] # # Three Outside Up/Down: values [0, -100, 100] # dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100] # # Three Inside Up/Down: values [0, -100, 100] # dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100] # # Chart type # # ------------------------------------ # # Heikin Ashi Strategy # heikinashi = qtpylib.heikinashi(dataframe) # dataframe['ha_open'] = heikinashi['open'] # dataframe['ha_close'] = heikinashi['close'] # dataframe['ha_high'] = heikinashi['high'] # dataframe['ha_low'] = heikinashi['low'] # Retrieve best bid and best ask from the orderbook # ------------------------------------ """ # first check if dataprovider is available if self.dp: if self.dp.runmode in ('live', 'dry_run'): ob = self.dp.orderbook(metadata['pair'], 1) dataframe['best_bid'] = ob['bids'][0][0] dataframe['best_ask'] = ob['asks'][0][0] """ return dataframe
def do_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Stoch fast - mainly due to 5m timeframes stoch_fast = ta.STOCHF(dataframe) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] #StochRSI for double checking things period = 14 smoothD = 3 SmoothK = 3 dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) stochrsi = (dataframe['rsi'] - dataframe['rsi'].rolling(period).min() ) / (dataframe['rsi'].rolling(period).max() - dataframe['rsi'].rolling(period).min()) dataframe['srsi_k'] = stochrsi.rolling(SmoothK).mean() * 100 dataframe['srsi_d'] = dataframe['srsi_k'].rolling(smoothD).mean() # Bollinger Bands because obviously bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=1) dataframe['bb_lowerband'] = bollinger['lower'] dataframe['bb_middleband'] = bollinger['mid'] dataframe['bb_upperband'] = bollinger['upper'] # SAR Parabol - probably don't need this dataframe['sar'] = ta.SAR(dataframe) ## confirm wideboi variance signal with bbw expansion dataframe["bb_width"] = ( (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]) dataframe['bbw_expansion'] = dataframe['bb_width'].rolling( window=4).apply(self.bbw_expansion) # confirm entry and exit on smoothed HA dataframe = self.HA(dataframe, 4) # thanks to Hansen_Khornelius for this idea that I apply to the 1hr informative # https://github.com/hansen1015/freqtrade_strategy hansencalc = self.hansen_HA(dataframe, 6) dataframe['emac'] = hansencalc['emac'] dataframe['emao'] = hansencalc['emao'] # money flow index (MFI) for in/outflow of money, like RSI adjusted for vol dataframe['mfi'] = fta.MFI(dataframe) ## sqzmi to detect quiet periods dataframe['sqzmi'] = fta.SQZMI(dataframe) #, MA=hansencalc['emac']) # Volume Flow Indicator (MFI) for volume based on the direction of price movement dataframe['vfi'] = fta.VFI(dataframe, period=14) dmi = fta.DMI(dataframe, period=14) dataframe['dmi_plus'] = dmi['DI+'] dataframe['dmi_minus'] = dmi['DI-'] dataframe['adx'] = fta.ADX(dataframe, period=14) ## for stoploss - all from Solipsis4 ## simple ATR and ROC for stoploss dataframe['atr'] = ta.ATR(dataframe, timeperiod=14) dataframe['roc'] = ta.ROC(dataframe, timeperiod=9) dataframe['rmi'] = RMI(dataframe, length=24, mom=5) ssldown, sslup = SSLChannels_ATR(dataframe, length=21) dataframe['sroc'] = SROC(dataframe, roclen=21, emalen=13, smooth=21) dataframe['ssl-dir'] = np.where(sslup > ssldown, 'up', 'down') dataframe['rmi-up'] = np.where( dataframe['rmi'] >= dataframe['rmi'].shift(), 1, 0) dataframe['rmi-up-trend'] = np.where( dataframe['rmi-up'].rolling(5).sum() >= 3, 1, 0) dataframe['candle-up'] = np.where( dataframe['close'] >= dataframe['close'].shift(), 1, 0) dataframe['candle-up-trend'] = np.where( dataframe['candle-up'].rolling(5).sum() >= 3, 1, 0) return dataframe
def populate_indicators(dataframe: DataFrame) -> DataFrame: """ Adds several different TA indicators to the given DataFrame """ dataframe['adx'] = ta.ADX(dataframe) dataframe['ao'] = qtpylib.awesome_oscillator(dataframe) dataframe['cci'] = ta.CCI(dataframe) macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] dataframe['mfi'] = ta.MFI(dataframe) dataframe['minus_dm'] = ta.MINUS_DM(dataframe) dataframe['minus_di'] = ta.MINUS_DI(dataframe) dataframe['plus_dm'] = ta.PLUS_DM(dataframe) dataframe['plus_di'] = ta.PLUS_DI(dataframe) dataframe['roc'] = ta.ROC(dataframe) dataframe['rsi'] = ta.RSI(dataframe) # Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy) rsi = 0.1 * (dataframe['rsi'] - 50) dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1) # Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy) dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1) # Stoch stoch = ta.STOCH(dataframe) dataframe['slowd'] = stoch['slowd'] dataframe['slowk'] = stoch['slowk'] # Stoch fast stoch_fast = ta.STOCHF(dataframe) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] # Stoch RSI stoch_rsi = ta.STOCHRSI(dataframe) dataframe['fastd_rsi'] = stoch_rsi['fastd'] dataframe['fastk_rsi'] = stoch_rsi['fastk'] # Bollinger bands bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband'] = bollinger['lower'] dataframe['bb_middleband'] = bollinger['mid'] dataframe['bb_upperband'] = bollinger['upper'] # EMA - Exponential Moving Average dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3) dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5) dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10) dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100) # SAR Parabolic dataframe['sar'] = ta.SAR(dataframe) # SMA - Simple Moving Average dataframe['sma'] = ta.SMA(dataframe, timeperiod=40) # TEMA - Triple Exponential Moving Average dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9) # Hilbert Transform Indicator - SineWave hilbert = ta.HT_SINE(dataframe) dataframe['htsine'] = hilbert['sine'] dataframe['htleadsine'] = hilbert['leadsine'] # Pattern Recognition - Bullish candlestick patterns # ------------------------------------ """ # Hammer: values [0, 100] dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe) # Inverted Hammer: values [0, 100] dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe) # Dragonfly Doji: values [0, 100] dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe) # Piercing Line: values [0, 100] dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100] # Morningstar: values [0, 100] dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100] # Three White Soldiers: values [0, 100] dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100] """ # Pattern Recognition - Bearish candlestick patterns # ------------------------------------ """ # Hanging Man: values [0, 100] dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe) # Shooting Star: values [0, 100] dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe) # Gravestone Doji: values [0, 100] dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe) # Dark Cloud Cover: values [0, 100] dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe) # Evening Doji Star: values [0, 100] dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe) # Evening Star: values [0, 100] dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe) """ # Pattern Recognition - Bullish/Bearish candlestick patterns # ------------------------------------ """ # Three Line Strike: values [0, -100, 100] dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe) # Spinning Top: values [0, -100, 100] dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100] # Engulfing: values [0, -100, 100] dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100] # Harami: values [0, -100, 100] dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100] # Three Outside Up/Down: values [0, -100, 100] dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100] # Three Inside Up/Down: values [0, -100, 100] dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100] """ # Chart type # ------------------------------------ # Heikinashi stategy heikinashi = qtpylib.heikinashi(dataframe) dataframe['ha_open'] = heikinashi['open'] dataframe['ha_close'] = heikinashi['close'] dataframe['ha_high'] = heikinashi['high'] dataframe['ha_low'] = heikinashi['low'] return dataframe
def populate_indicators(self, dataframe: DataFrame, 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: if not metadata['pair'] in self.custom_trade_info: self.custom_trade_info[metadata['pair']] = {} c = Consensus(dataframe) # Overlap / MA's c.evaluate_tema(period=12) c.evaluate_ema(period=6) c.evaluate_ema(period=12) c.evaluate_sma(period=12) c.evaluate_ichimoku() # slightly slow #c.evaluate_hull() # very slow c.evaluate_vwma(period=20) # Oscillators c.evaluate_rsi(period=14) c.evaluate_stoch() #c.evaluate_cci() # slightly slow #c.evaluate_adx() c.evaluate_macd() c.evaluate_momentum() c.evaluate_williams() # c.evaluate_ultimate_oscilator() # extremely slow # missing: awesome osc # missing: bull bear # missing: stoch rsi #c.evaluate_macd_cross_over() c.evaluate_osc() c.evaluate_cmf() c.evaluate_cmo() # slightly slow #c.evaluate_laguerre() # slow dataframe['consensus-buy'] = c.score()['buy'] dataframe['consensus-sell'] = c.score()['sell'] # Indicators for ROI and Custom Stoploss dataframe['atr'] = ta.ATR(dataframe, timeperiod=24) dataframe['roc'] = ta.ROC(dataframe, timeperiod=9) # Trend Indicators for ROI dataframe['rmi-slow'] = cta.RMI(dataframe, length=21, mom=5) dataframe['rmi-up'] = 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) # 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) cbtc = Consensus(btc_stake_tf) cbtc.evaluate_tema(period=12) cbtc.evaluate_ema(period=12) cbtc.evaluate_ema(period=24) cbtc.evaluate_sma(period=12) cbtc.evaluate_ichimoku() cbtc.evaluate_vwma(period=20) cbtc.evaluate_rsi(period=14) cbtc.evaluate_stoch() cbtc.evaluate_macd() cbtc.evaluate_momentum() cbtc.evaluate_williams() cbtc.evaluate_osc() cbtc.evaluate_cmf() cbtc.evaluate_cmo() cbtc.evaluate_laguerre() dataframe['BTC_consensus-buy'] = cbtc.score()['buy'] # BTC/STAKE - Informative Timeframe # btc_stake_inf_tf = self.dp.get_pair_dataframe(pair=btc_stake, timeframe=self.inf_timeframe) # 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'): # Attempting to use a temporary holding place for dynamic roi backtest return value... if not 'backtest' in self.custom_trade_info: self.custom_trade_info['backtest'] = {} 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') self.custom_trade_info[ metadata['pair']]['consensus-buy'] = dataframe[[ 'date', 'consensus-buy' ]].copy().set_index('date') self.custom_trade_info[ metadata['pair']]['consensus-sell'] = dataframe[[ 'date', 'consensus-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: if not metadata['pair'] in self.custom_trade_info: self.custom_trade_info[metadata['pair']] = {} ## Base Timeframe / Pair dataframe['kama'] = ta.KAMA(dataframe, length=233) # RMI: https://www.tradingview.com/script/kwIt9OgQ-Relative-Momentum-Index/ dataframe['rmi'] = cta.RMI(dataframe, length=24, mom=5) # Momentum Pinball: https://www.tradingview.com/script/fBpVB1ez-Momentum-Pinball-Indicator/ dataframe['roc-mp'] = ta.ROC(dataframe, timeperiod=1) dataframe['mp'] = ta.RSI(dataframe['roc-mp'], timeperiod=3) # MA Streak: https://www.tradingview.com/script/Yq1z7cIv-MA-Streak-Can-Show-When-a-Run-Is-Getting-Long-in-the-Tooth/ dataframe['mastreak'] = cta.mastreak(dataframe, period=4) # Percent Change Channel: https://www.tradingview.com/script/6wwAWXA1-MA-Streak-Change-Channel/ upper, mid, lower = cta.pcc(dataframe, period=40, mult=3) dataframe['pcc-lowerband'] = lower dataframe['pcc-upperband'] = upper lookup_idxs = dataframe.index.values - ( abs(dataframe['mastreak'].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'] # Trends, Peaks and Crosses dataframe['candle-up'] = np.where( dataframe['close'] >= dataframe['close'].shift(), 1, 0) dataframe['candle-up-trend'] = np.where( dataframe['candle-up'].rolling(5).sum() >= 3, 1, 0) dataframe['rmi-up'] = np.where( dataframe['rmi'] >= dataframe['rmi'].shift(), 1, 0) dataframe['rmi-up-trend'] = np.where( dataframe['rmi-up'].rolling(5).sum() >= 3, 1, 0) dataframe['rmi-dn'] = np.where( dataframe['rmi'] <= dataframe['rmi'].shift(), 1, 0) dataframe['rmi-dn-count'] = dataframe['rmi-dn'].rolling(8).sum() dataframe['streak-bo'] = np.where( dataframe['streak-roc'] < dataframe['pcc-lowerband'], 1, 0) dataframe['streak-bo-count'] = dataframe['streak-bo'].rolling(8).sum() # Indicators used only for ROI and Custom Stoploss ssldown, sslup = cta.SSLChannels_ATR(dataframe, length=21) dataframe['sroc'] = cta.SROC(dataframe, roclen=21, emalen=13, smooth=21) dataframe['ssl-dir'] = np.where(sslup > ssldown, 'up', 'down') # 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 1d low to set up guards informative['1d-high'] = informative['close'].rolling(24).max() informative['1d-low'] = informative['close'].rolling(24).min() informative['adr'] = informative['1d-high'] - informative['1d-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=55, 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=55, mom=5) # 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: self.custom_btc_inf = True # 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=55, mom=5) dataframe['BTC_close'] = btc_stake_tf['close'] dataframe['BTC_kama'] = ta.KAMA(btc_stake_tf, length=144) # 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']]['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