From d21c2cebeb76c172c39182ac1e25b0c30e56d745 Mon Sep 17 00:00:00 2001 From: MoonGem <34537029+MoonGem@users.noreply.github.com> Date: Sun, 25 Mar 2018 06:17:48 -0500 Subject: [PATCH] Delete defauult_random_strategy.py --- .../strategies/defauult_random_strategy.py | 338 ------------------ 1 file changed, 338 deletions(-) delete mode 100644 user_data/strategies/defauult_random_strategy.py diff --git a/user_data/strategies/defauult_random_strategy.py b/user_data/strategies/defauult_random_strategy.py deleted file mode 100644 index 358288b2f..000000000 --- a/user_data/strategies/defauult_random_strategy.py +++ /dev/null @@ -1,338 +0,0 @@ - -# --- Do not remove these libs --- -from freqtrade.strategy.interface import IStrategy -from typing import Dict, List -from hyperopt import hp -from functools import reduce -from pandas import DataFrame -# -------------------------------- - -# Add your lib to import here -import talib.abstract as ta -import freqtrade.vendor.qtpylib.indicators as qtpylib -import numpy # noqa - -import random - -# Update this variable if you change the class name -class_name = 'DefaultStrategy' - - -# This class is a sample. Feel free to customize it. - - - - -def Select(): - param = [] - random_items = [] - param.append(str('[' + 'uptrend_long_ema' + '[' + 'enabled' + ']')) - param.append(str('[' + 'macd_below_zero' + '][' + 'enabled' + ']')) - param.append(str('[' + 'uptrend_short_ema' '][' + 'enabled'+ ']')) - param.append(str('[' + 'mfi' '][' + 'enabled'+ ']')) - param.append(str('[' + 'fastd' '][' + 'enabled'+ ']')) - param.append(str('[' + 'adx' '][' + 'enabled'+ ']')) - param.append(str('[' + 'rsi' '][' + 'enabled'+ ']')) - param.append(str('[' + 'over_sar' '][' + 'enabled'+ ']')) - param.append(str('[' + 'green_candle' '][' + 'enabled'+ ']')) - param.append(str('[' + 'uptrend_sma' '][' + 'enabled'+ ']')) - param.append(str('[' + 'closebb' '][' + 'enabled'+ ']')) - param.append(str('[' + 'temabb' '][' + 'enabled'+ ']')) - param.append(str('[' + 'fastdt' '][' + 'enabled'+ ']')) - param.append(str('[' + 'ao' '][' + 'enabled'+ ']')) - param.append(str('[' + 'ema3' '][' + 'enabled'+ ']')) - param.append(str('[' + 'macd' '][' + 'enabled'+ ']')) - param.append(str('[' + 'closesar' '][' + 'enabled'+ ']')) - param.append(str('[' + 'htsine' '][' + 'enabled'+ ']')) - param.append(str('[' + 'has' '][' + 'enabled'+ ']')) - param.append(str('[' + 'plusdi' '][' + 'enabled'+ ']')) - howmany = random.randint(1,20) - random_items = random.choices(population=param, k=howmany) - print(' ') - print('The Parameters Enabled Are As Follows!!!: ' + str(random_items)) - print(' ') - return random_items - - - - -class DefaultStrategy(IStrategy): - """ - This is a test strategy to inspire you. - More information in https://github.com/gcarq/freqtrade/blob/develop/docs/bot-optimization.md - - You can: - - 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 - """ - - # Minimal ROI designed for the strategy. - # This attribute will be overridden if the config file contains "minimal_roi" - minimal_roi = { - "40": 0.0, - "30": 0.01, - "20": 0.02, - "0": 0.04 - } - - ticker_interval = 5 - - # Optimal stoploss designed for the strategy - # This attribute will be overridden if the config file contains "stoploss" - stoploss = -0.10 - - def populate_indicators(self, dataframe: DataFrame) -> 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. - """ - - # Momentum Indicator - # ------------------------------------ - - # ADX - dataframe['adx'] = ta.ADX(dataframe) - - - # Awesome oscillator - dataframe['ao'] = qtpylib.awesome_oscillator(dataframe) - - # Commodity Channel Index: values Oversold:<-100, Overbought:>100 - dataframe['cci'] = ta.CCI(dataframe) - - # MACD - macd = ta.MACD(dataframe) - dataframe['macd'] = macd['macd'] - dataframe['macdsignal'] = macd['macdsignal'] - dataframe['macdhist'] = macd['macdhist'] - - # MFI - dataframe['mfi'] = ta.MFI(dataframe) - - # Minus Directional Indicator / Movement - dataframe['minus_dm'] = ta.MINUS_DM(dataframe) - dataframe['minus_di'] = ta.MINUS_DI(dataframe) - - # Plus Directional Indicator / Movement - dataframe['plus_dm'] = ta.PLUS_DM(dataframe) - dataframe['plus_di'] = ta.PLUS_DI(dataframe) - dataframe['minus_di'] = ta.MINUS_DI(dataframe) - - # ROC - dataframe['roc'] = ta.ROC(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'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1) - - # Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy) - dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1) - - # Stoch - stoch = ta.STOCH(dataframe) - dataframe['slowd'] = stoch['slowd'] - dataframe['slowk'] = stoch['slowk'] - - # Stoch fast - stoch_fast = ta.STOCHF(dataframe) - dataframe['fastd'] = stoch_fast['fastd'] - dataframe['fastk'] = stoch_fast['fastk'] - - # Stoch RSI - stoch_rsi = ta.STOCHRSI(dataframe) - dataframe['fastd_rsi'] = stoch_rsi['fastd'] - dataframe['fastk_rsi'] = stoch_rsi['fastk'] - - - # Overlap Studies - # ------------------------------------ - - """ - # Previous Bollinger bands - # Because ta.BBANDS implementation is broken with small numbers, it actually - # returns middle band for all the three bands. Switch to qtpylib.bollinger_bands - # and use middle band instead. - - dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband'] - """ - - # 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'] - - - # 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['ema50'] = ta.EMA(dataframe, timeperiod=50) - dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100) - - # SAR Parabol - dataframe['sar'] = ta.SAR(dataframe) - - # SMA - Simple Moving Average - dataframe['sma'] = ta.SMA(dataframe, timeperiod=40) - - - # 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 - # ------------------------------------ - - # Heikinashi stategy - heikinashi = qtpylib.heikinashi(dataframe) - dataframe['ha_open'] = heikinashi['open'] - dataframe['ha_close'] = heikinashi['close'] - dataframe['ha_high'] = heikinashi['high'] - dataframe['ha_low'] = heikinashi['low'] - - - return dataframe - - params = Select() - valm = random.randint(1,100) - valfast = random.randint(1,100) - valadx = random.randint(1,100) - valrsi = random.randint(1,100) - def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame: - - conditions = [] - # GUARDS AND TRENDS - if 'uptrend_long_ema' in str(self.params): - conditions.append(dataframe['ema50'] > dataframe['ema100']) - if 'macd_below_zero' in str(self.params): - conditions.append(dataframe['macd'] < 0) - if 'uptrend_short_ema' in str(self.params): - conditions.append(dataframe['ema5'] > dataframe['ema10']) - if 'mfi' in str(self.params): - print('MFI Value: ' + str(self.valm)) - conditions.append(dataframe['mfi'] < self.valm) - if 'fastd' in str(self.params): - print('FASTD Value :' + str(self.valfast)) - conditions.append(dataframe['fastd'] < self.valfast) - if 'adx' in str(self.params): - print('ADX Value :' + str(self.valadx)) - conditions.append(dataframe['adx'] > self.valadx) - if 'rsi' in str(self.params): - print('RSI Value :' + str(self.valrsi)) - conditions.append(dataframe['rsi'] < self.valrsi) - if 'over_sar' in str(self.params): - conditions.append(dataframe['close'] > dataframe['sar']) - if 'green_candle' in str(self.params): - conditions.append(dataframe['close'] > dataframe['open']) - if 'uptrend_sma' in str(self.params): - prevsma = dataframe['sma'].shift(1) - conditions.append(dataframe['sma'] > prevsma) - if 'closebb' in str(self.params): - conditions.append(dataframe['close'] < dataframe['bb_lowerband']) - if 'temabb' in str(self.params): - conditions.append(dataframe['tema'] < dataframe['bb_lowerband']) - if 'fastdt' in str(self.params): - conditions.append(qtpylib.crossed_above(dataframe['fastd'], 10.0)) - if 'ao' in str(self.params): - conditions.append(qtpylib.crossed_above(dataframe['ao'], 0.0)) - if 'ema3' in str(self.params): - conditions.append(qtpylib.crossed_above(dataframe['ema3'], dataframe['ema10'])) - if 'macd' in str(self.params): - conditions.append(qtpylib.crossed_above(dataframe['macd'], dataframe['macdsignal'])) - if 'closesar' in str(self.params): - conditions.append(qtpylib.crossed_above(dataframe['close'], dataframe['sar'])) - if 'htsine' in str(self.params): - conditions.append(qtpylib.crossed_above(dataframe['htleadsine'], dataframe['htsine'])) - if 'has' in str(self.params): - conditions.append((qtpylib.crossed_above(dataframe['ha_close'], dataframe['ha_open'])) & (dataframe['ha_low'] == dataframe['ha_open'])) - if 'plusdi' in str(self.params): - conditions.append(qtpylib.crossed_above(dataframe['plus_di'], dataframe['minus_di'])) - - dataframe.loc[ - reduce(lambda x, y: x & y, conditions), - 'buy'] = 1 - - return dataframe - - def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame: - """ - Based on TA indicators, populates the sell signal for the given dataframe - :param dataframe: DataFrame - :return: DataFrame with buy column - """ - dataframe.loc[ - ( - ), - 'sell'] = 1 - return dataframe -