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141 lines
4.7 KiB
Python
141 lines
4.7 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 IStrategy
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class StrategyTestV2(IStrategy):
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"""
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Strategy used by tests 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 = {"40": 0.0, "30": 0.01, "20": 0.02, "0": 0.04}
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# Optimal stoploss designed for the strategy
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stoploss = -0.10
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# Optimal timeframe 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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"entry": "limit",
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"exit": "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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"entry": "gtc",
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"exit": "gtc",
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}
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# Test legacy use_sell_signal definition
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use_sell_signal = False
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# By default this strategy does not use Position Adjustments
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position_adjustment_enable = False
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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"] < 35)
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& (dataframe["fastd"] < 35)
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& (dataframe["adx"] > 30)
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& (dataframe["plus_di"] > 0.5)
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)
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| ((dataframe["adx"] > 65) & (dataframe["plus_di"] > 0.5)),
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"buy",
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] = 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 buy 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"], 70))
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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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| ((dataframe["adx"] > 70) & (dataframe["minus_di"] > 0.5)),
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"sell",
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] = 1
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return dataframe
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