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Jérôme Delacotte
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Zeus.py
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Zeus.py
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# Zeus Strategy: First Generation of GodStra Strategy with maximum
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# AVG/MID profit in USDT
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# Author: @Mablue (Masoud Azizi)
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# github: https://github.com/mablue/
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# IMPORTANT: INSTALL TA BEFOUR RUN(pip install ta)
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# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --spaces buy sell roi --strategy Zeus
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# --- Do not remove these libs ---
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from freqtrade.strategy.parameters import CategoricalParameter, DecimalParameter
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from freqtrade.strategy.interface import IStrategy
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from pandas import DataFrame
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# --------------------------------
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# Add your lib to import here
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import ta
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from functools import reduce
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import numpy as np
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import talib.abstract as talib
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from freqtrade.strategy.strategy_helper import merge_informative_pair
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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class Zeus(IStrategy):
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# * 1/43: 86 trades. 72/6/8 Wins/Draws/Losses. Avg profit 12.66%. Median profit 11.99%. Total profit 0.10894395 BTC ( 108.94Σ%). Avg duration 3 days, 0:31:00 min. Objective: -48.48793
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# "max_open_trades": 10,
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# "stake_currency": "BTC",
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# "stake_amount": 0.01,
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# "tradable_balance_ratio": 0.99,
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# "timeframe": "4h",
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# "dry_run_wallet": 0.1,
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# Buy hyperspace params:
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buy_params = {
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"buy_cat": "<R",
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"buy_real": 0.0128,
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}
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# Sell hyperspace params:
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sell_params = {
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"sell_cat": "=R",
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"sell_real": 0.9455,
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}
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# ROI table:
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minimal_roi = {
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"0": 0.564,
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"567": 0.273,
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"2814": 0.12,
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"7675": 0
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}
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# Stoploss:
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stoploss = -0.256
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buy_real = DecimalParameter(0.001, 0.999, decimals=4, default=0.11908, space='buy')
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buy_cat = CategoricalParameter([">R", "=R", "<R"], default='<R', space='buy')
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buy_pct = DecimalParameter(0.001, 0.02, decimals=3, default=0.005, space='buy')
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buy_pct_1 = DecimalParameter(-0.2, 0.2, decimals=2, default=0.005, space='buy')
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buy_pct_3 = DecimalParameter(-0.2, 0.2, decimals=2, default=0.005, space='buy')
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buy_pct_5 = DecimalParameter(-0.2, 0.2, decimals=2, default=0.005, space='buy')
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buy_bb_lowerband = DecimalParameter(1, 1.05, default=1, decimals=2, space='buy')
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buy_bb_width = DecimalParameter(0.01, 0.15, default=0.065, decimals=2, space='buy')
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# sell_real = DecimalParameter(0.001, 0.999, decimals=4, default=0.59608, space='sell')
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# sell_cat = CategoricalParameter([">R", "=R", "<R"], default='>R', space='sell')
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# Buy hypers
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timeframe = '4h'
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plot_config = {
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"main_plot": {
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"min200": {
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"color": "#86c932",
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}
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},
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"subplots": {
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"Ind": {
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"trend_ichimoku_base": {
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"color": "#dd1384",
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},
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"trend_kst_diff": {
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"color": "#850678",
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},
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},
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"Percent": {
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"max_min": {
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"color": "#74effc",
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}
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}
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}
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}
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def informative_pairs(self):
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# get access to all pairs available in whitelist.
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pairs = self.dp.current_whitelist()
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# Assign tf to each pair so they can be downloaded and cached for strategy.
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# informative_pairs = [(pair, "5m") for pair in pairs]
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informative_pairs = [(pair, '1d') for pair in pairs]
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# informative_pairs += [(pair, '1w') for pair in pairs]
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# Optionally Add additional "static" pairs
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# informative_pairs = [("BTC/USDT", "1w"), ("BTC/USDT", "1d"), ("BTC/USDT", "5m")]
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return informative_pairs
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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# Add all ta features
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dataframe['trend_ichimoku_base'] = ta.trend.ichimoku_base_line(
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dataframe['high'],
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dataframe['low'],
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window1=9,
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window2=26,
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visual=False,
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fillna=False
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)
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KST = ta.trend.KSTIndicator(
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close=dataframe['close'],
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roc1=10,
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roc2=15,
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roc3=20,
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roc4=30,
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window1=10,
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window2=10,
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window3=10,
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window4=15,
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nsig=9,
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fillna=False
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)
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dataframe['trend_kst_diff'] = KST.kst_diff()
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dataframe['pct_change'] = dataframe['close'].pct_change(5)
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dataframe['min10'] = talib.MIN(dataframe['close'], timeperiod=10)
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dataframe['min20'] = talib.MIN(dataframe['close'], timeperiod=20)
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dataframe['min50'] = talib.MIN(dataframe['close'], timeperiod=50)
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dataframe['min200'] = talib.MIN(dataframe['close'], timeperiod=200)
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# Normalization
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tib = dataframe['trend_ichimoku_base']
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dataframe['trend_ichimoku_base'] = (tib-tib.min())/(tib.max()-tib.min())
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tkd = dataframe['trend_kst_diff']
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dataframe['trend_kst_diff'] = (tkd-tkd.min())/(tkd.max()-tkd.min())
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informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe="1d")
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informative["rsi"] = talib.RSI(informative)
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informative["max3"] = talib.MAX(informative['close'], timeperiod=3)
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informative["min3"] = talib.MIN(informative['close'], timeperiod=3)
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informative['pct_change_1'] = informative['close'].pct_change(1)
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informative['pct_change_3'] = informative['close'].pct_change(3)
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informative['pct_change_5'] = informative['close'].pct_change(5)
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bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(informative), window=20, stds=2)
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informative['bb_lowerband'] = bollinger['lower']
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informative['bb_middleband'] = bollinger['mid']
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informative['bb_upperband'] = bollinger['upper']
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informative["bb_percent"] = (
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(informative["close"] - informative["bb_lowerband"]) /
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(informative["bb_upperband"] - informative["bb_lowerband"])
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)
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informative["bb_width"] = (
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(informative["bb_upperband"] - informative["bb_lowerband"]) / informative["bb_middleband"]
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)
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dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1d", ffill=True)
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return dataframe
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def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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conditions = []
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IND = 'trend_ichimoku_base'
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REAL = self.buy_real.value
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OPR = self.buy_cat.value
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DFIND = dataframe[IND]
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# print(DFIND.mean())
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if OPR == ">R":
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conditions.append(DFIND > REAL)
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elif OPR == "=R":
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conditions.append(np.isclose(DFIND, REAL))
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elif OPR == "<R":
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conditions.append(DFIND < REAL)
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if conditions:
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dataframe.loc[
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(reduce(lambda x, y: x & y, conditions))
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& (dataframe['pct_change'] < - self.buy_pct.value)
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& (dataframe['close'] <= dataframe['min50'] * 1.002)
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& (dataframe['pct_change_1_1d'] > self.buy_pct_1.value)
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& (dataframe['pct_change_3_1d'] > self.buy_pct_3.value)
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& (dataframe['pct_change_5_1d'] > self.buy_pct_5.value)
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#& (dataframe['close_1d'] < dataframe['bb_lowerband_1d'] * self.buy_bb_lowerband.value)
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& (dataframe['bb_width_1d'] >= self.buy_bb_width.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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# conditions = []
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# IND = 'trend_kst_diff'
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# REAL = self.sell_real.value
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# OPR = self.sell_cat.value
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# DFIND = dataframe[IND]
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# # print(DFIND.mean())
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#
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# if OPR == ">R":
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# conditions.append(DFIND > REAL)
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# elif OPR == "=R":
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# conditions.append(np.isclose(DFIND, REAL))
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# elif OPR == "<R":
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# conditions.append(DFIND < REAL)
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#
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# if conditions:
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# dataframe.loc[
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# reduce(lambda x, y: x & y, conditions),
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# 'sell'] = 1
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return dataframe
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