# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # flake8: noqa: F401 # --- Do not remove these libs --- import numpy as np # noqa import pandas as pd # noqa from pandas import DataFrame from freqtrade.strategy import ( BooleanParameter, CategoricalParameter, DecimalParameter, IStrategy, IntParameter, ) # -------------------------------- # Add your lib to import here import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib class BBRSINaiveStrategy(IStrategy): """ This is a strategy template to get you started. More information in https://www.freqtrade.io/en/latest/strategy-customization/ You can: :return: a Dataframe with all mandatory indicators for the strategies - Rename the class name (Do not forget to update class_name) - Add any methods you want to build your strategy - Add any lib you need to build your strategy You must keep: - the lib in the section "Do not remove these libs" - the methods: populate_indicators, populate_buy_trend, populate_sell_trend You should keep: - timeframe, minimal_roi, stoploss, trailing_* """ # 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 = {"0": 0.236, "87": 0.052, "163": 0.018, "466": 0} # Optimal stoploss designed for the strategy. # This attribute will be overridden if the config file contains "stoploss". stoploss = -0.257 # Trailing stoploss trailing_stop = True trailing_stop_positive = 0.041 trailing_stop_positive_offset = 0.044 trailing_only_offset_is_reached = True # 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 = 30 # 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"}, }, "subplots": { # Subplots - each dict defines one additional plot "MACD": { "macd": {"color": "blue"}, "macdsignal": {"color": "orange"}, }, "RSI": { "rsi": {"color": "red"}, }, }, } 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 """ # RSI dataframe["rsi"] = ta.RSI(dataframe) # MACD macd = ta.MACD(dataframe) dataframe["macd"] = macd["macd"] dataframe["macdsignal"] = macd["macdsignal"] dataframe["macdhist"] = macd["macdhist"] # Bollinger Bands bollinger = qtpylib.bollinger_bands( qtpylib.typical_price(dataframe), window=50, stds=1 ) 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"] # RSI dataframe["rsi"] = ta.RSI(dataframe, window=30) # Parabolic SAR dataframe["sar"] = ta.SAR(dataframe) # TEMA - Triple Exponential Moving Average dataframe["tema"] = ta.TEMA(dataframe, timeperiod=9) # Retrieve best bid and best ask from the orderbook # ------------------------------------ """ # first check if dataprovider is available if self.dp: if self.dp.runmode.value 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 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"], 55)) # # (dataframe["rsi"] > 25) # & ( # qtpylib.crossed_above(dataframe["macd"], (dataframe["macdsignal"])) # | qtpylib.crossed_above( # dataframe["close"], dataframe["bb_upperband"] # ) # ) # # & (dataframe["close"] > dataframe["bb_upperband"]) # # & (dataframe["close"] > dataframe["bb_middleband"]) # # & (dataframe["volume"] > 0) # ), # "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_below(dataframe["rsi"], 70)) # # (dataframe["rsi"] > 70) # # & (dataframe["macdhist"] < 0) # # | qtpylib.crossed_below(dataframe["macd"], dataframe["macdsignal"]) # | qtpylib.crossed_below(dataframe["close"], dataframe["bb_upperband"]) # # & (dataframe["volume"] > 0) # Make sure Volume is not 0 # ), # "sell", # ] = 1 # 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 ), "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 ), "sell", ] = 1 return dataframe