275 lines
11 KiB
Python
275 lines
11 KiB
Python
# Heracles Strategy: Strongest Son of GodStra
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# ( With just 1 Genome! its a bacteria :D )
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# Author: @Mablue (Masoud Azizi)
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# github: https://github.com/mablue/
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# IMPORTANT:Add to your pairlists inside config.json (Under StaticPairList):
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# {
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# "method": "AgeFilter",
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# "min_days_listed": 100
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# },
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# IMPORTANT: INSTALL TA BEFOUR RUN(pip install ta)
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#
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# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --spaces roi buy --strategy Heracles
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# ######################################################################
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# --- Do not remove these libs ---
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from freqtrade.strategy.parameters import IntParameter, 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 talib.abstract as ta
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import pandas as pd
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import talib.abstract as talib
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import ta
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from ta.utils import dropna
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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from functools import reduce
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import numpy as np
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from freqtrade.strategy.strategy_helper import merge_informative_pair
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class Heracles_2(IStrategy):
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########################################## RESULT PASTE PLACE ##########################################
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# 10/100: 25 trades. 18/4/3 Wins/Draws/Losses. Avg profit 5.92%. Median profit 6.33%. Total profit 0.04888306 BTC ( 48.88Σ%). Avg duration 4 days, 6:24:00 min. Objective: -11.42103
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# Buy hyperspace params:
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buy_params = {
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"buy_crossed_indicator_shift": 9,
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"buy_div_max": 0.75,
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"buy_div_min": 0.16,
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"buy_indicator_shift": 15,
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}
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# Sell hyperspace params:
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sell_params = {
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}
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# ROI table:
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minimal_roi = {
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"0": 0.598,
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"644": 0.166,
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"3269": 0.115,
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"7289": 0
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}
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# Stoploss:
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stoploss = -0.256
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# Optimal timeframe use it in your config
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timeframe = '4h'
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# Trailing stoploss
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trailing_stop = True
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trailing_stop_positive = 0.001
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trailing_stop_positive_offset = 0.015
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trailing_only_offset_is_reached = True
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plot_config = {
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"main_plot": {
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"max50": {
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"color": "#dd1384",
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"type": "line"
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},
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"min50": {
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"color": "#850678",
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"type": "line"
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},
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"min3_1d": {
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"color": "#30c310",
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"type": "line"
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},
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"max3_1d": {
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"color": "#e64062",
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"type": "line"
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},
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"min200": {
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"color": "#86c932",
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"type": "line"
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}
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},
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"subplots": {
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"Vol": {
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"volume10": {
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"color": "#7b53f5",
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"type": "line"
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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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"type": "line"
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}
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}
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}
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}
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########################################## END RESULT PASTE PLACE ######################################
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# buy params
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buy_div_min = DecimalParameter(0, 1, default=0.16, decimals=2, space='buy')
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buy_div_max = DecimalParameter(0, 1, default=0.75, decimals=2, space='buy')
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buy_indicator_shift = IntParameter(0, 20, default=16, space='buy')
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buy_crossed_indicator_shift = IntParameter(0, 20, default=9, space='buy')
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#buy_rsi_min = IntParameter(10, 40, default=10, space='buy')
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#buy_rsi_max = IntParameter(50, 98, default=90, space='buy')
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buy_max_min = DecimalParameter(0, 0.05, default=0.02, decimals=2, 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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def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
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current_profit: float, **kwargs):
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dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
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last_candle = dataframe.iloc[-1].squeeze()
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previous_last_candle = dataframe.iloc[-2].squeeze()
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previous_5_candle = dataframe.iloc[-5].squeeze()
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if (current_profit > 0.01) \
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& ((previous_5_candle['sma5'] > last_candle['sma5']) \
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| (last_candle['percent3'] < -0.01) | (last_candle['percent5'] < -0.01)) \
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& ((last_candle['percent'] < 0) & (last_candle['percent3'] < 0)):
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# print("over_bb_band_sma10_desc", pair, trade, " profit=", current_profit, " rate=", current_rate)
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return 'sma10'
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if (current_profit > 0.01) \
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& ((previous_5_candle['sma10'] > last_candle['sma10']) \
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| (last_candle['percent3'] < -0.01) | (last_candle['percent5'] < -0.01)) \
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& ((last_candle['percent'] < 0) & (last_candle['percent3'] < 0)):
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# print("over_bb_band_sma10_desc", pair, trade, " profit=", current_profit, " rate=", current_rate)
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return 'sma10'
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if (current_profit > 0.005) \
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& (previous_last_candle['sma10'] > last_candle['sma10']) \
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& ((current_time - trade.open_date_utc).seconds >= 3600) \
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& ((previous_last_candle['sma20'] > last_candle['sma20']) &
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((last_candle['percent5'] < 0) | (last_candle['percent10'] < 0) | (last_candle['percent20'] < 0))):
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# print("over_bb_band_sma10_desc", pair, trade, " profit=", current_profit, " rate=", current_rate)
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return 'sma20'
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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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dataframe = dropna(dataframe)
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dataframe['volatility_kcw'] = ta.volatility.keltner_channel_wband(
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dataframe['high'],
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dataframe['low'],
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dataframe['close'],
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window=20,
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window_atr=10,
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fillna=False,
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original_version=True
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)
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dataframe['volatility_dcp'] = ta.volatility.donchian_channel_pband(
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dataframe['high'],
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dataframe['low'],
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dataframe['close'],
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window=10,
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offset=0,
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fillna=False
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)
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dataframe['rsi'] = talib.RSI(dataframe)
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dataframe['sma5'] = talib.SMA(dataframe, timeperiod=5)
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dataframe['sma10'] = talib.SMA(dataframe, timeperiod=10)
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dataframe['sma20'] = talib.SMA(dataframe, timeperiod=20)
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dataframe['sma50'] = talib.SMA(dataframe, timeperiod=50)
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dataframe['sma100'] = talib.SMA(dataframe, timeperiod=100)
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dataframe["percent"] = (dataframe["close"] - dataframe["open"]) / dataframe["open"]
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dataframe["percent5"] = dataframe["percent"].rolling(5).sum()
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dataframe["percent3"] = dataframe["percent"].rolling(3).sum()
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dataframe["percent10"] = dataframe["percent"].rolling(10).sum()
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dataframe["percent20"] = dataframe["percent"].rolling(20).sum()
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dataframe["percent50"] = dataframe["percent"].rolling(50).sum()
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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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dataframe["volume10"] = dataframe["volume"].rolling(10).mean()
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dataframe['max50'] = talib.MAX(dataframe['close'], timeperiod=50)
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dataframe['max_min'] = (dataframe['max50'] - dataframe['min50']) / dataframe['min50']
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dataframe["volume10"] = dataframe["volume"].rolling(10).mean()
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dataframe['volume_cond'] = (dataframe["volume10"] * dataframe['close'] / 1000 >= 19)
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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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dataframe["bb_percent"] = (
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(dataframe["close"] - dataframe["bb_lowerband"]) /
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(dataframe["bb_upperband"] - dataframe["bb_lowerband"])
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)
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dataframe["bb_width"] = (
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(dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]
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)
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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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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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"""
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Buy strategy Hyperopt will build and use.
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"""
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conditions = []
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IND = 'volatility_dcp'
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CRS = 'volatility_kcw'
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DFIND = dataframe[IND]
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DFCRS = dataframe[CRS]
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d = DFIND.shift(self.buy_indicator_shift.value).div(
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DFCRS.shift(self.buy_crossed_indicator_shift.value))
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# print(d.min(), "\t", d.max())
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conditions.append(
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d.between(self.buy_div_min.value, self.buy_div_max.value))
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if conditions:
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dataframe.loc[
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(
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(dataframe['volume10'] * dataframe['close'] / 1000 >= 19)
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& (reduce(lambda x, y: x & y, conditions))
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& (dataframe['close_1d'] <= dataframe['open_1d'])
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& (dataframe['max_min'] >= self.buy_max_min.value)
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& (dataframe['close'] <= dataframe['min200'] * 1.005)
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& (dataframe['close'].shift(1) <= dataframe['min200'].shift(1) * 1.005)
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& (dataframe['min200'].shift(1) == dataframe['min200'])
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& (dataframe['min50'] <= dataframe['min3_1d'] * 1.005)
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& (dataframe['close'].shift(1) < dataframe['bb_lowerband'] * self.buy_bb_lowerband.value)
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& (dataframe['bb_width'].shift(1) >= 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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"""
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Sell strategy Hyperopt will build and use.
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"""
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# dataframe.loc[
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# (dataframe['close'] >= dataframe['max50'] * 0.998)
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# , 'sell'] = 1
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return dataframe
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