diff --git a/user_data/strategies/1. defaultBB +6609%.py b/user_data/strategies/1. defaultBB +6609%.py deleted file mode 100644 index 474776834..000000000 --- a/user_data/strategies/1. defaultBB +6609%.py +++ /dev/null @@ -1,346 +0,0 @@ -# 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 - - -class defaultB(IStrategy): - """ - This is a strategy template to get you started. - More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md - - 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 prototype for the methods: minimal_roi, stoploss, populate_indicators, populate_buy_trend, - populate_sell_trend, hyperopt_space, buy_strategy_generator - """ - # 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 = { - "4320": 0.0018, - #"300": 0.01, - #"60": 0.04, # 0.01 - #"30": 0.06, # 0.02 - "0": 0.30 # 0.04 - } - - # Optimal stoploss designed for the strategy. - # This attribute will be overridden if the config file contains "stoploss". - stoploss = -0.0521 # -0.05 - - # Trailing stoploss - trailing_stop = True - trailing_only_offset_is_reached = True - - # trailing_stop_positive = 0.005 - trailing_stop_positive = 0.0005 - trailing_stop_positive_offset = 0.034 - - # Optimal timeframe for the strategy. - timeframe = '30m' - - # Run "populate_indicators()" only for new candle. - process_only_new_candles = False - - # These values canconfig222 be overridden in the "ask_strategy" section in the config. - use_sell_signal = False - sell_profit_only = True - ignore_roi_if_buy_signal = True - - # Number of candles the strategy requires before producing valid signals - startup_candle_count: int = 200 - - # 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 - """ - - # Momentum Indicators - # ------------------------------------ - - # ADX - dataframe['adx'] = ta.ADX(dataframe) - dataframe['adx50'] = ta.ADX(dataframe, timeperiod=50) - - - # # Commodity Channel Index: values [Oversold:-100, Overbought:100] - dataframe['cci'] = ta.CCI(dataframe) - dataframe['cmo'] = ta.CMO(dataframe) - - # RSI - dataframe['rsi'] = ta.RSI(dataframe) - dataframe['rsi25'] = ta.RSI(dataframe, timeperiod=25) - dataframe['rsi50'] = ta.RSI(dataframe, timeperiod=50) - dataframe['rsi100'] = ta.RSI(dataframe, timeperiod=100) - dataframe['rsi200'] = ta.RSI(dataframe, timeperiod=200) - dataframe['rsi400'] = ta.RSI(dataframe, timeperiod=400) - dataframe['rsi1000'] = ta.RSI(dataframe, timeperiod=1000) - - - # Stochastic Fast - stoch_fast = ta.STOCHF(dataframe) - dataframe['fastd'] = stoch_fast['fastd'] - dataframe['fastk'] = stoch_fast['fastk'] - - - # MACD - macd = ta.MACD(dataframe) - dataframe['macd'] = macd['macd'] - dataframe['macdsignal'] = macd['macdsignal'] - dataframe['macdhist'] = macd['macdhist'] - - # MFI - dataframe['mfi'] = ta.MFI(dataframe) - dataframe['mfi20'] = ta.MFI(dataframe, timeperiod=20) - dataframe['mfi50'] = ta.MFI(dataframe, timeperiod=50) - dataframe['mfi100'] = ta.MFI(dataframe, timeperiod=100) - dataframe['mfi200'] = ta.MFI(dataframe, timeperiod=200) - dataframe['mfi400'] = ta.MFI(dataframe, timeperiod=400) - dataframe['mfi1000'] = ta.MFI(dataframe, timeperiod=1000) - - # # 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"] - ) - - # # 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) - dataframe['ema200'] = ta.EMA(dataframe, timeperiod=100) - dataframe['ema400'] = ta.EMA(dataframe, timeperiod=100) - - - # Parabolic SAR - dataframe['sar'] = ta.SAR(dataframe) - - # TEMA - Triple Exponential Moving Average - dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9) - dataframe['tema3'] = ta.TEMA(dataframe, timeperiod=3) - dataframe['tema5'] = ta.TEMA(dataframe, timeperiod=5) - dataframe['tema14'] = ta.TEMA(dataframe, timeperiod=14) - dataframe['tema21'] = ta.TEMA(dataframe, timeperiod=21) - dataframe['tema50'] = ta.TEMA(dataframe, timeperiod=50) - - # Cycle Indicator - # ------------------------------------ - # Hilbert Transform Indicator - SineWave - hilbert = ta.HT_SINE(dataframe) - dataframe['htsine'] = hilbert['sine'] - dataframe['htleadsine'] = hilbert['leadsine'] - - # # Chart type - # # ------------------------------------ - # helps with pattern recognition - dataframe['average'] = (dataframe['close'] + dataframe['open'] + dataframe['high'] + dataframe['low']) / 4 - dataframe['sma_slow'] = ta.SMA(dataframe, timeperiod=200, price='close') - dataframe['sma_medium'] = ta.SMA(dataframe, timeperiod=100, price='close') - dataframe['sma_fast'] = ta.SMA(dataframe, timeperiod=50, price='close') - - # Retrieve best bid and best ask from the orderbook - # ------------------------------------ - """ - # first check if dataprovider is available - if self.dp: - if self.dp.runmode 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[ # Breakout: TEMA crosses over longer TEMA - ( - (qtpylib.crossed_above(dataframe['tema5'], dataframe['tema14'])) & - (dataframe['adx'] > 25) - ), - 'buy'] = 1 - dataframe.loc[ # Breakout: RSI plus momentum - ( - (qtpylib.crossed_above(dataframe['rsi'], 60)) & - (dataframe['adx'] > 30) - ), - 'buy'] = 1 - dataframe.loc[ # Breakout: crossing middle of Bollinger Bands - ( - (qtpylib.crossed_above(dataframe['close'], dataframe['bb_middleband'])) & - (dataframe['adx'] > 30) & - (dataframe['adx'] > dataframe['adx'].shift(1)) & - (dataframe['adx'].shift(1) > dataframe['adx'].shift(2)) & - (dataframe['adx'].shift(2) > dataframe['adx'].shift(3)) & - (dataframe['close'] > dataframe['ema21']) & - (dataframe['close'] > dataframe['ema50']) & - (dataframe['roc'] > 0) & - (dataframe['ema50'] > 0) & - (dataframe['ema21'] > dataframe['ema50']) - ), - 'buy'] = 1 - dataframe.loc[ # Breakout: Increasing momentum and positive price action - ( - (qtpylib.crossed_above(dataframe['adx'], 25)) & - (dataframe['ema21'] > dataframe['ema50']) - ), - 'buy'] = 1 - dataframe.loc[ # BTFD: CCI bounce - # Doesn't like BTC Dominance tracking sideways - # Enable by uncommenting the indicators - ( - #(qtpylib.crossed_above(dataframe['cci'], -100)) - ), - 'buy'] = 1 - dataframe.loc[ # BTFD: Price Crosses into the Bollinger Bands from below - # Doesn't like BTC Dominance tracking sideways - # Enable by uncommenting the indicators - ( - #(qtpylib.crossed_above(dataframe['close'], dataframe['bb_lowerband'])) - ), - 'buy'] = 1 - dataframe.loc[ # BTFD: V-shaped bottom. - # Doesn't like BTC Dominance tracking sideways - # Enable by uncommenting the indicators - ( - #(dataframe['average'].shift(5) > dataframe['average'].shift(4)) & - #(dataframe['average'].shift(4) > dataframe['average'].shift(3)) & - #(dataframe['average'].shift(3) > dataframe['average'].shift(2)) & - #(dataframe['average'].shift(2) > dataframe['average'].shift(1)) & - #(dataframe['average'].shift(1) < dataframe['average'].shift(0)) & - #(dataframe['low'].shift(1) < dataframe['bb_middleband']) & - #(dataframe['cci'].shift(1) < -100) - ), - '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['mfi'], 95)) # Signal: RSI crosses above 70 - ), - 'sell'] = 1 - dataframe.loc[ - ( - (dataframe['mfi'] > 90) & # Signal: RSI crosses above 70 - (dataframe['tema'] < dataframe['tema'].shift(3)) & # Guard: negative trend - (dataframe['volume'] > 0) # Make sure Volume is not 0 - ), - 'sell'] = 1 - dataframe.loc[ - ( - (qtpylib.crossed_above(dataframe['close'], dataframe['bb_upperband'])) & - (dataframe['mfi'] > 60) # High MFI - ), - 'sell'] = 1 - dataframe.loc[ - ( - (qtpylib.crossed_below(dataframe['adx'], 28)) & - (dataframe['rsi'] > 60) # High RSI - ), - 'sell'] = 1 - dataframe.loc[ - ( - (qtpylib.crossed_below(dataframe['adx'], 28)) & - (dataframe['mfi'] > 60) # High MFI - ), - 'sell'] = 1 - return dataframe -