247 lines
8.5 KiB
Python
247 lines
8.5 KiB
Python
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
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import talib.abstract as ta
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from pandas import DataFrame
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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from freqtrade.strategy import (BooleanParameter, DecimalParameter, IntParameter, IStrategy,
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RealParameter)
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class HyperoptableStrategy(IStrategy):
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"""
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Default Strategy provided by freqtrade bot.
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Please do not modify this strategy, it's intended for internal use only.
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Please look at the SampleStrategy in the user_data/strategy directory
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or strategy repository https://github.com/freqtrade/freqtrade-strategies
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for samples and inspiration.
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"""
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INTERFACE_VERSION = 2
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# Minimal ROI designed for the strategy
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minimal_roi = {
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"40": 0.0,
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"30": 0.01,
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"20": 0.02,
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"0": 0.04
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}
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# Optimal stoploss designed for the strategy
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stoploss = -0.10
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# Optimal ticker interval for the strategy
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timeframe = '5m'
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# Optional order type mapping
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order_types = {
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'buy': 'limit',
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'sell': 'limit',
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'stoploss': 'limit',
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'stoploss_on_exchange': False
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}
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# Number of candles the strategy requires before producing valid signals
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startup_candle_count: int = 20
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# Optional time in force for orders
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order_time_in_force = {
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'buy': 'gtc',
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'sell': 'gtc',
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}
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buy_params = {
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'buy_rsi': 35,
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# Intentionally not specified, so "default" is tested
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# 'buy_plusdi': 0.4
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}
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sell_params = {
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'sell_rsi': 74,
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'sell_minusdi': 0.4
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}
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short_params = {
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'short_rsi': 65,
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}
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exit_short_params = {
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'exit_short_rsi': 26,
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'exit_short_minusdi': 0.6
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}
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buy_rsi = IntParameter([0, 50], default=30, space='buy')
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buy_plusdi = RealParameter(low=0, high=1, default=0.5, space='buy')
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sell_rsi = IntParameter(low=50, high=100, default=70, space='sell')
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sell_minusdi = DecimalParameter(low=0, high=1, default=0.5001, decimals=3, space='sell',
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load=False)
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protection_enabled = BooleanParameter(default=True)
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protection_cooldown_lookback = IntParameter([0, 50], default=30)
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@property
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def protections(self):
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prot = []
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if self.protection_enabled.value:
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prot.append({
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"method": "CooldownPeriod",
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"stop_duration_candles": self.protection_cooldown_lookback.value
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})
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return prot
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short_rsi = IntParameter([50, 100], default=70, space='sell')
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short_plusdi = RealParameter(low=0, high=1, default=0.5, space='sell')
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exit_short_rsi = IntParameter(low=0, high=50, default=30, space='buy')
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exit_short_minusdi = DecimalParameter(low=0, high=1, default=0.4999, decimals=3, space='buy',
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load=False)
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def informative_pairs(self):
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"""
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Define additional, informative pair/interval combinations to be cached from the exchange.
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These pair/interval combinations are non-tradeable, unless they are part
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of the whitelist as well.
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For more information, please consult the documentation
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:return: List of tuples in the format (pair, interval)
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Sample: return [("ETH/USDT", "5m"),
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("BTC/USDT", "15m"),
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]
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"""
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return []
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Adds several different TA indicators to the given DataFrame
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Performance Note: For the best performance be frugal on the number of indicators
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you are using. Let uncomment only the indicator you are using in your strategies
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or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
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:param dataframe: Dataframe with data from the exchange
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:param metadata: Additional information, like the currently traded pair
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:return: a Dataframe with all mandatory indicators for the strategies
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"""
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# Momentum Indicator
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# ------------------------------------
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# ADX
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dataframe['adx'] = ta.ADX(dataframe)
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# MACD
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macd = ta.MACD(dataframe)
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dataframe['macd'] = macd['macd']
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dataframe['macdsignal'] = macd['macdsignal']
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dataframe['macdhist'] = macd['macdhist']
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# Minus Directional Indicator / Movement
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dataframe['minus_di'] = ta.MINUS_DI(dataframe)
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# Plus Directional Indicator / Movement
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dataframe['plus_di'] = ta.PLUS_DI(dataframe)
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# RSI
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dataframe['rsi'] = ta.RSI(dataframe)
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# Stoch fast
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stoch_fast = ta.STOCHF(dataframe)
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dataframe['fastd'] = stoch_fast['fastd']
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dataframe['fastk'] = stoch_fast['fastk']
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# Bollinger bands
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bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
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dataframe['bb_lowerband'] = bollinger['lower']
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dataframe['bb_middleband'] = bollinger['mid']
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dataframe['bb_upperband'] = bollinger['upper']
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# EMA - Exponential Moving Average
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dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
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return dataframe
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def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Based on TA indicators, populates the buy signal for the given dataframe
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:param dataframe: DataFrame
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:param metadata: Additional information, like the currently traded pair
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:return: DataFrame with buy column
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"""
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dataframe.loc[
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(
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(dataframe['rsi'] < self.buy_rsi.value) &
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(dataframe['fastd'] < 35) &
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(dataframe['adx'] > 30) &
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(dataframe['plus_di'] > self.buy_plusdi.value)
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) |
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(
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(dataframe['adx'] > 65) &
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(dataframe['plus_di'] > self.buy_plusdi.value)
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),
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'buy'] = 1
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return dataframe
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def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Based on TA indicators, populates the sell signal for the given dataframe
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:param dataframe: DataFrame
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:param metadata: Additional information, like the currently traded pair
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:return: DataFrame with sell column
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"""
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dataframe.loc[
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(
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(
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(qtpylib.crossed_above(dataframe['rsi'], self.sell_rsi.value)) |
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(qtpylib.crossed_above(dataframe['fastd'], 70))
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) &
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(dataframe['adx'] > 10) &
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(dataframe['minus_di'] > 0)
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) |
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(
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(dataframe['adx'] > 70) &
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(dataframe['minus_di'] > self.sell_minusdi.value)
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),
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'sell'] = 1
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return dataframe
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def populate_short_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Based on TA indicators, populates the short signal for the given dataframe
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:param dataframe: DataFrame
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:param metadata: Additional information, like the currently traded pair
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:return: DataFrame with short column
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"""
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dataframe.loc[
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(
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(dataframe['rsi'] > self.short_rsi.value) &
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(dataframe['fastd'] > 65) &
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(dataframe['adx'] < 70) &
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(dataframe['plus_di'] < self.short_plusdi.value)
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) |
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(
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(dataframe['adx'] < 35) &
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(dataframe['plus_di'] < self.short_plusdi.value)
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),
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'short'] = 1
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return dataframe
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def populate_exit_short_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Based on TA indicators, populates the exit_short signal for the given dataframe
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:param dataframe: DataFrame
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:param metadata: Additional information, like the currently traded pair
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:return: DataFrame with exit_short column
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"""
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dataframe.loc[
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(
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(
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(qtpylib.crossed_below(dataframe['rsi'], self.exit_short_rsi.value)) |
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(qtpylib.crossed_below(dataframe['fastd'], 30))
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) &
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(dataframe['adx'] < 90) &
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(dataframe['minus_di'] < 0) # TODO-lev: What should this be
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) |
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(
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(dataframe['adx'] < 30) &
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(dataframe['minus_di'] < self.exit_short_minusdi.value)
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),
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'exit_short'] = 1
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return dataframe
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