diff --git a/freqtrade/templates/sample_short_strategy.py b/freqtrade/templates/sample_short_strategy.py deleted file mode 100644 index 6be46430b..000000000 --- a/freqtrade/templates/sample_short_strategy.py +++ /dev/null @@ -1,383 +0,0 @@ -# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement -# flake8: noqa: F401 -# isort: skip_file -# --- 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 - - -# TODO: Create a meaningfull short strategy (not just revresed signs). -# This class is a sample. Feel free to customize it. -class SampleShortStrategy(IStrategy): - """ - This is a sample strategy to inspire you. - 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_entry_trend, populate_exit_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 = 3 - - # Can this strategy go short? - can_short: bool = True - - # 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 - # trailing_only_offset_is_reached = False - # trailing_stop_positive = 0.01 - # trailing_stop_positive_offset = 0.0 # Disabled / not configured - - # Hyperoptable parameters - short_rsi = IntParameter(low=51, high=100, default=70, space='sell', optimize=True, load=True) - exit_short_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=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 = { - 'entry': 'limit', - 'exit': 'limit', - 'stoploss': 'market', - 'stoploss_on_exchange': False - } - - # Optional order time in force. - order_time_in_force = { - 'entry': 'gtc', - 'exit': 'gtc' - } - - plot_config = { - 'main_plot': { - 'tema': {}, - 'sar': {'color': 'white'}, - }, - 'subplots': { - "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 - """ - - # 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 - # Please read https://github.com/freqtrade/freqtrade/issues/2961 before using this. - # STOCHRSI is NOT aligned with tradingview, which may result in non-expected results. - # 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.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_entry_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[ - ( - # Signal: RSI crosses above 70 - (qtpylib.crossed_above(dataframe['rsi'], self.short_rsi.value)) & - (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 - ), - 'enter_short'] = 1 - - return dataframe - - def populate_exit_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 sell column - """ - - dataframe.loc[ - ( - # Signal: RSI crosses above 30 - (qtpylib.crossed_above(dataframe['rsi'], self.exit_short_rsi.value)) & - # Guard: tema below BB middle - (dataframe['tema'] <= dataframe['bb_middleband']) & - (dataframe['tema'] > dataframe['tema'].shift(1)) & # Guard: tema is raising - (dataframe['volume'] > 0) # Make sure Volume is not 0 - ), - 'exit_short'] = 1 - - return dataframe