# source: https://raw.githubusercontent.com/Fractate/freqbot/b98419e2793a0cfe4418d5fad85071da833cb4fc/user_data/strategies/STRATEGY_RSI_BB_BOUNDS_CROSS.py # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # --- Do not remove these libs --- import numpy as np # noqa import pandas as pd # noqa from pandas import DataFrame from freqtrade.strategy.interface import IStrategy # -------------------------------- # Add your lib to import here import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib _trend_length = 14 _bb_smooth_length=4 def iff(a,b,c): if a: return b else: return c class github_Fractate_freqbot__STRATEGY_RSI_BB_BOUNDS_CROSS__20210923_020346(IStrategy): """ Strategy RSI_BB_BOUNDS_CROSS author@: Fractate_Dev github@: https://github.com/Fractate/freqbot How to use it? > python3 ./freqtrade/main.py -s RSI_BB_BOUNDS_CROSS """ # Strategy interface version - allow new iterations of the strategy interface. # Check the documentation or the Sample strategy to get the latest version. INTERFACE_VERSION = 2 # Minimal ROI designed for the strategy. # This attribute will be overridden if the config file contains "minimal_roi". minimal_roi = { "60": 0.01, "30": 0.02, "0": 0.04 } # Optimal stoploss designed for the strategy. # This attribute will be overridden if the config file contains "stoploss". stoploss = -0.10 # Trailing stoploss trailing_stop = False # Optimal timeframe for the strategy. timeframe = '5m' # Run "populate_indicators()" only for new candle. process_only_new_candles = False # These values can be overridden in the "ask_strategy" section in the config. use_sell_signal = True sell_profit_only = False ignore_roi_if_buy_signal = False # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 20 # Optional order type mapping. order_types = { 'buy': 'limit', 'sell': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False } # Optional order time in force. order_time_in_force = { 'buy': 'gtc', 'sell': 'gtc' } plot_config = { # Main plot indicators (Moving averages, ...) 'main_plot': { 'tema': {}, 'sar': {'color': 'white'}, 'bb_ub': {'color': 'green'}, 'bb_lb': {'color': 'green'}, 'bb_lb_smoothed': {'color': 'red'}, 'rsi_ub': {'color': 'black'}, 'rsi_lb': {'color': 'black'}, 'rsi_lb_smoothed': {'color': 'orange'}, }, 'subplots': { # Subplots - each dict defines one additional plot # "MACD": { # 'macd': {'color': 'blue'}, # 'macdsignal': {'color': 'orange'}, # }, # "BB": { # 'bb_percent': {'color': 'red'}, # '1': {}, # '0': {}, # }, # "RSI": { # 'rsi': {'color': 'red'}, # '70': {}, # '30': {}, # }, # "RSI_Percent": { # 'rsi_percent': {'color': 'red'}, # '1': {}, # '0': {}, # }, # "bb_above": { # 'bb_above_rsi': {} # }, # "bb_below": { # 'bb_below_rsi': {} # }, # "bb_minus_rsi_percent": { # 'bb_minus_rsi_percent': {}, # '0': {}, # }, "bb_rsi_count": { 'ub_bb_over_rsi_trend': {}, 'lb_bb_under_rsi_trend': {}, }, } } def informative_pairs(self): """ Define additional, informative pair/interval combinations to be cached from the exchange. These pair/interval combinations are non-tradeable, unless they are part of the whitelist as well. For more information, please consult the documentation :return: List of tuples in the format (pair, interval) Sample: return [("ETH/USDT", "5m"), ("BTC/USDT", "15m"), ] """ return [] 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 """ # remove later for i in range(1): #print("") # Logic # Bollinger Band getting less than RSI should occur after a certain number of candles pass # time frame of 1 hour # Bollinger Bands bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=14, stds=2) dataframe['bb_lb'] = bollinger['lower'] dataframe['bb_middleband'] = bollinger['mid'] dataframe['bb_ub'] = bollinger['upper'] dataframe['bb_lb_smoothed'] = 0 for i in range(_bb_smooth_length): dataframe['bb_lb_smoothed'] += dataframe['bb_lb'].shift(i)/_bb_smooth_length dataframe['bb_percent'] = (dataframe['close'] - bollinger['lower']) / (bollinger['upper'] - bollinger['lower']) # RSI dataframe['rsi'] = ta.RSI(dataframe) # rsi(prices, period \\ 14) dataframe['70'] = 70 dataframe['30'] = 30 dataframe['1'] = 1 dataframe['0'] = 0 rsi_limit = 30 dataframe['rsi_percent'] = (dataframe['rsi'] - rsi_limit) / (100 - rsi_limit * 2) # Convert RSI Percentage to actual value in candlestick data # ep = 2 * length - 1 # auc = ema( max( src - src[1], 0 ), ep ) # adc = ema( max( src[1] - src, 0 ), ep ) # x1 = (length - 1) * ( adc * obLevel / (100-obLevel) - auc) # ub = iff( x1 >= 0, src + x1, src + x1 * (100-obLevel)/obLevel ) # x2 = (length - 1) * ( adc * osLevel / (100-osLevel) - auc) # lb = iff( x2 >= 0, src + x2, src + x2 * (100-osLevel)/osLevel ) length = 14 ep = 2 * length - 1 dataframe.loc[(dataframe['close'] - dataframe['close'].shift(1)) > 0, 'auc1'] = (dataframe['close'] - dataframe['close'].shift(1)) dataframe.loc[(dataframe['close'] - dataframe['close'].shift(1)) <= 0, 'auc1'] = 0 dataframe['auc'] = ta.EMA(dataframe['auc1'], ep) # dataframe['adc'] = ta.EMA(ta.MAX(dataframe['close'].shift(1) - dataframe['close']), ep) dataframe.loc[(dataframe['close'].shift(1) - dataframe['close']) > 0, 'adc1'] = (dataframe['close'].shift(1) - dataframe['close']) dataframe.loc[(dataframe['close'].shift(1) - dataframe['close']) <= 0, 'adc1'] = 0 dataframe['adc'] = ta.EMA(dataframe['adc1'], ep) dataframe['x1'] = (length - 1) * ( dataframe['adc'] * 70 / (100-70) - dataframe['auc'] ) dataframe['x1'] = np.nan_to_num(dataframe['x1']) # dataframe['ub'] = iff( dataframe['x1'] >= 0, dataframe['close'] + dataframe['x1'], dataframe['close'] + dataframe['x1'] * (100-70)/70 ) dataframe.loc[dataframe['x1'] >= 0, 'rsi_ub'] = dataframe['close'] + dataframe['x1'] dataframe.loc[dataframe['x1'] < 0, 'rsi_ub'] = dataframe['close'] + dataframe['x1'] * (100-70)/70 dataframe['x2'] = (length - 1) * ( dataframe['adc'] * 30 / (100-30) - dataframe['auc'] ) dataframe.loc[dataframe['x2'] >= 0, 'rsi_lb'] = dataframe['close'] + dataframe['x2'] dataframe.loc[dataframe['x2'] < 0, 'rsi_lb'] = dataframe['close'] + dataframe['x2'] * (100-30)/30 dataframe['rsi_lb_smoothed'] = 0 for i in range(_bb_smooth_length): dataframe['rsi_lb_smoothed'] += dataframe['rsi_lb'].shift(i)/_bb_smooth_length # auc = ema( max( src - src[1], 0 ), ep ) # adc = ema( max( src[1] - src, 0 ), ep ) # x1 = (length - 1) * ( adc * obLevel / (100-obLevel) - auc) # ub = iff( x1 >= 0, src + x1, src + x1 * (100-obLevel)/obLevel ) # x2 = (length - 1) * ( adc * osLevel / (100-osLevel) - auc) # lb = iff( x2 >= 0, src + x2, src + x2 * (100-osLevel)/osLevel ) dataframe['bb_minus_rsi_percent'] = dataframe['bb_percent'] - dataframe['rsi_percent'] dataframe['ub_bb_over_rsi'] = dataframe['bb_ub'] > dataframe['rsi_ub'] dataframe['lb_bb_under_rsi'] = dataframe['bb_lb'] < dataframe['rsi_lb'] dataframe['ub_bb_over_rsi_trend'] = True dataframe['lb_bb_under_rsi_trend'] = True # Verify RSI positivity or negativity over trend # dataframe['bb_above_rsi_count'] = True # dataframe['bb_below_rsi_count'] = True for i in range(_trend_length): # dataframe['bb_above_rsi_count'] = (dataframe['bb_minus_rsi_percent'].shift(i) > 0) & dataframe['bb_above_rsi_count'] # dataframe['bb_below_rsi_count'] = (dataframe['bb_minus_rsi_percent'].shift(i) < 0) & dataframe['bb_below_rsi_count'] dataframe['ub_bb_over_rsi_trend'] = dataframe['ub_bb_over_rsi'].shift(i) & dataframe['ub_bb_over_rsi_trend'] dataframe['lb_bb_under_rsi_trend'] = dataframe['lb_bb_under_rsi'].shift(i) & dataframe['lb_bb_under_rsi_trend'] dataframe['ema14'] = ta.EMA(dataframe, timeperiod=14) dataframe['ema2'] = ta.EMA(dataframe, timeperiod=2) ## old method # bb_above_rsi = False # bb_below_rsi = False # dataframe['bb_above_rsi_count'] = 0 # dataframe['bb_below_rsi_count'] = 0 # for i in range(_trend_length): # if np.where(dataframe['bb_percent'].shift(i) < dataframe['rsi_percent'].shift(i)): # dataframe['bb_below_rsi_count'] += 1 # if np.where(dataframe['bb_percent'].shift(i) > dataframe['rsi_percent'].shift(i)): # dataframe['bb_above_rsi_count'] += 1 # if bb_above_rsi == True & bb_below_rsi == False: # dataframe['bb_above_rsi'] = 1 # if bb_above_rsi == False & bb_below_rsi == True: # dataframe['bb_below_rsi'] = 1 # #print(type(dataframe['bb_below_rsi'])) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the buy signal for the given dataframe :param dataframe: DataFrame populated with indicators :param metadata: Additional information, like the currently traded pair :return: DataFrame with buy column """ dataframe.loc[ ( # (qtpylib.crossed_above(dataframe['rsi'], 30)) & # Signal: RSI crosses above 30 # (dataframe['tema'] <= dataframe['bb_middleband']) & # Guard: tema below BB middle # (dataframe['tema'] > dataframe['tema'].shift(1)) & # Guard: tema is raising # (dataframe['volume'] > 0) # Make sure Volume is not 0 # qtpylib.crossed_above(dataframe['bb_lb'], dataframe['rsi_lb']) & # if rsi trend has been above or low for a certain amount of time (dataframe['lb_bb_under_rsi_trend']) & # # if close is higher than rsi lower bound # (dataframe['close'] > dataframe['rsi_lb']) & # (dataframe['close'].shift(1) < dataframe['rsi_lb'].shift(1)) & # if bb lower bound is less than rsi lower bound (dataframe['bb_lb'] < dataframe['rsi_lb']) & (dataframe['bb_lb'].shift(1) < dataframe['rsi_lb'].shift(1)) & # # if bb lb is higher than it was before # (dataframe['bb_lb'] > dataframe['bb_lb'].shift(1)) & # (dataframe['bb_lb'].shift(1) < dataframe['bb_lb'].shift(2)) & # if ema2 is rising (dataframe['ema2'] > dataframe['ema2'].shift(1)) # # dataframes are less than 5 percent, though this shouldn't matter due to ema2 comparison # (dataframe['bb_percent'] < 0.5) & # (dataframe['rsi_percent'] < 0.5) ), 'buy'] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the sell signal for the given dataframe :param dataframe: DataFrame populated with indicators :param metadata: Additional information, like the currently traded pair :return: DataFrame with buy column """ dataframe.loc[ ( # (qtpylib.crossed_above(dataframe['rsi'], 70)) & # Signal: RSI crosses above 70 # (dataframe['tema'] > dataframe['bb_middleband']) & # Guard: tema above BB middle # (dataframe['tema'] < dataframe['tema'].shift(1)) & # Guard: tema is falling # (dataframe['volume'] > 0) # Make sure Volume is not 0 # qtpylib.crossed_below(dataframe['bb_percent'], dataframe['rsi_percent']) & # qtpylib.crossed_below(dataframe['bb_ub'], dataframe['rsi_ub']) & # (dataframe['close'] < dataframe['rsi_ub']) & # (dataframe['close'].shift(1) > dataframe['rsi_ub'].shift(1)) & # (dataframe['bb_ub'] > dataframe['rsi_ub']) & # (dataframe['bb_ub'].shift(1) > dataframe['rsi_ub'].shift(1)) # (dataframe['bb_percent'] > 0.5) & # (dataframe['rsi_percent'] > 0.5) & # (dataframe['ub_bb_over_rsi_trend'].shift(1)) # (qtpylib.crossed_above(dataframe['rsi'], 50)) # Signal: RSI crosses above 70 # (qtpylib.crossed_above(dataframe['close'], dataframe['ema14'])) # Signal: RSI crosses above 70 # if ema2 is falling (dataframe['ema2'] < dataframe['ema2'].shift(1)) ), 'sell'] = 1 return dataframe