304 lines
13 KiB
Python
304 lines
13 KiB
Python
# pr#agma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
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# isort: skip_file
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# --- Do not remove these libs ---
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from datetime import datetime
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import numpy as np # noqa
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import pandas as pd # noqa
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from freqtrade.strategy.parameters import DecimalParameter, BooleanParameter, IntParameter
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from pandas import DataFrame
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import math
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from functools import reduce
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from freqtrade.strategy.interface import IStrategy
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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 freqtrade.vendor.qtpylib.indicators as qtpylib
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from freqtrade.strategy.strategy_helper import merge_informative_pair
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# This class is a sample. Feel free to customize it.
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class StrategyJD_5_2(IStrategy):
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# Strategy interface version - allow new iterations of the strategy interface.
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# Check the documentation or the Sample strategy to get the latest version.
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INTERFACE_VERSION = 2
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# valeur de bbwidth pour démarrer
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# buy_bollinger = DecimalParameter(0.025, 0.125, decimals=2, default=0.07, space="buy")
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#buy_msma_10 = DecimalParameter(0.997, 1.020, decimals=3, default=0.998, space="buy")
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# pourcentage sma à dépasser
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# buy_sma_percent = DecimalParameter(0.95, 1.05, decimals=2, default=0.97, space="buy")
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buy_decalage = IntParameter(1, 24, default=5, space="buy")
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buy_min_max_n = DecimalParameter(0, 0.2, decimals=2, default=0.05, space='buy')
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buy_rsi_min = IntParameter(0, 50, default=25, space="buy")
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buy_rsi_max = IntParameter(50, 100, default=60, space="buy")
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n_percent = IntParameter(1, 12, default=1, space="protection")
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percent_sell = DecimalParameter(-0.2, -0.01, decimals=2, default=-0.08, space="protection")
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# buy_adx_enabled = BooleanParameter(default=True, space="buy")
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# buy_rsi_enabled = CategoricalParameter([True, False], default=False, space="buy")
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# buy_trigger = CategoricalParameter(["bb_lower", "macd_cross_signal"], default="bb_lower", space="buy")
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# ROI table:
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minimal_roi = {
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# "0": 0.015
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"0": 0.5
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}
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# Stoploss:
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stoploss = -1
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trailing_stop = True
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trailing_stop_positive = 0.001
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trailing_stop_positive_offset = 0.0175 # 0.015
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trailing_only_offset_is_reached = True
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# max_open_trades = 3
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# Optimal ticker interval for the strategy.
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timeframe = '5m'
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# Run "populate_indicators()" only for new candle.
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process_only_new_candles = False
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# These values can be overridden in the "ask_strategy" section in the config.
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use_sell_signal = True
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sell_profit_only = False
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ignore_roi_if_buy_signal = False
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# Number of candles the strategy requires before producing valid signals
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startup_candle_count: int = 30
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# Optional order type mapping.
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order_types = {
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'buy': 'limit',
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'sell': 'limit',
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'stoploss': 'market',
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'stoploss_on_exchange': False
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}
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# Optional order time in force.
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order_time_in_force = {
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'buy': 'gtc',
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'sell': 'gtc'
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}
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plot_config = {
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# Main plot indicators (Moving averages, ...)
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'main_plot': {
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'bb_lowerband': {'color': 'white'},
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'bb_upperband': {'color': 'white'},
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'min200': {'color': 'yellow'},
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'min200_001': {'color': 'yellow'},
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},
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'subplots': {
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# Subplots - each dict defines one additional plot
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"BB": {
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'bb_width': {'color': 'white'},
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},
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"ADX": {
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'adx': {'color': 'white'},
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'minus_dm': {'color': 'blue'},
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'plus_dm': {'color': 'red'}
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},
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"rolling": {
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'bb_rolling': {'color': '#87e470'},
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"bb_rolling_min": {'color': '#ac3e2a'}
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}
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}
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}
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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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if last_candle['percent' + str(self.n_percent.value)] < self.percent_sell.value:
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return 'sell_lost_percent' + str(self.n_percent.value)
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# Between 2% and 10%, sell if EMA-long above EMA-short
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# if 0.02 < current_profit < 0.1:
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# if last_candle['ema100'] > last_candle['ema10']:
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# return 'ema_long_below_80'
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#
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# # Sell any positions at a loss if they are held for more than one day.
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# if current_profit < -0.20 and (current_time - trade.open_date_utc).days >= 3:
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# return 'unclog'
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# def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
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# proposed_stake: float, min_stake: float, max_stake: float,
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# **kwargs) -> float:
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#
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# dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
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# current_candle = dataframe.iloc[-1].squeeze()
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#
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# if dataframe['open'] < dataframe['sma100'] * 0.98:
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# # if self.config['stake_amount'] == 'unlimited':
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# # # Use entire available wallet during favorable conditions when in compounding mode.
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# # return max_stake
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# # else:
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# # Compound profits during favorable conditions instead of using a static stake.
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# return self.wallets.get_total_stake_amount() / self.config['max_open_trades']
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#
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# # Use default stake amount.
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# return proposed_stake
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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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# informative_pairs = [(pair, '1d') for pair in pairs]
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# informative_pairs += [(pair, '4h') for pair in pairs]
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informative_pairs = [(pair, '1h') for pair in pairs]
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return informative_pairs
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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# MACD
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# macd = ta.MACD(dataframe)
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# dataframe['macd'] = macd['macd']
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# dataframe['macdsignal'] = macd['macdsignal']
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# dataframe['macdhist'] = macd['macdhist']
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# # Plus Directional Indicator / Movement
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# dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
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# dataframe['plus_di'] = ta.PLUS_DI(dataframe)
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# Minus Directional Indicator / Movement
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# dataframe['adx'] = ta.ADX(dataframe)
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# dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
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# dataframe['minus_di'] = ta.MINUS_DI(dataframe)
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dataframe['min'] = ta.MIN(dataframe)
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dataframe['max'] = ta.MAX(dataframe)
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dataframe['min50'] = ta.MIN(dataframe['close'], timeperiod=50)
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dataframe['min200'] = ta.MIN(dataframe['close'], timeperiod=200)
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dataframe['min200_001'] = dataframe['min200'] * 1.002
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dataframe['max50'] = ta.MAX(dataframe['close'], timeperiod=50)
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dataframe['max200'] = ta.MAX(dataframe['close'], timeperiod=200)
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dataframe['min_max200'] = (dataframe['max200'] - dataframe['min200']) / dataframe['min200']
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dataframe['min_max50'] = (dataframe['max50'] - dataframe['min50']) / dataframe['min50']
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for n in range(1, 25):
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dataframe["percent" + str(n)] = dataframe['close'].pct_change(n)
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# # Aroon, Aroon Oscillator
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# aroon = ta.AROON(dataframe)
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# dataframe['aroonup'] = aroon['aroonup']
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# dataframe['aroondown'] = aroon['aroondown']
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# dataframe['aroonosc'] = ta.AROONOSC(dataframe)
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# RSI
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dataframe['rsi'] = ta.RSI(dataframe)
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# # EMA - Exponential Moving Average
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# dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
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# dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
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# dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
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# dataframe['mema10_3'] = dataframe['ema10'] / dataframe['ema10'].shift(1)
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# dataframe['mema10_5'] = dataframe['ema10'].rolling(5).mean()
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# dataframe['ema21'] = ta.EMA(dataframe, timeperiod=21)
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# dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
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# dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
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# # SMA - Simple Moving Average
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# dataframe['sma3'] = ta.SMA(dataframe, timeperiod=3)
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# dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5)
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dataframe['sma10'] = ta.SMA(dataframe, timeperiod=10)
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# dataframe['sma21'] = ta.SMA(dataframe, timeperiod=21)
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# dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50)
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dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100)
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dataframe['msma10_3'] = dataframe['sma10'] / dataframe['sma10'].shift(1)
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# dataframe['sma200'] = ta.SMA(dataframe, timeperiod=200)
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# dataframe['sma200_95'] = ta.SMA(dataframe, timeperiod=200) * 0.95
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# dataframe['sma200_98'] = ta.SMA(dataframe, timeperiod=200) * 0.98
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# dataframe['sma500'] = ta.SMA(dataframe, timeperiod=500)
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# dataframe['sma500_90'] = ta.SMA(dataframe, timeperiod=500) * 0.9
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# dataframe['sma500_95'] = ta.SMA(dataframe, timeperiod=500) * 0.95
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# dataframe['sma500_20'] = ta.SMA(dataframe, timeperiod=500) * 0.2
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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 1h
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informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe="1h")
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informative["rsi"] = ta.RSI(informative)
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informative["rsi3"] = ta.RSI(informative, 3)
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# informative["mrsi3"] = informative["rsi"].rolling(3).mean()
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# informative["max3"] = ta.MAX(informative['close'], timeperiod=3)
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# informative["min3"] = ta.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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# informative['sma3'] = ta.SMA(informative, timeperiod=3)
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# informative['sma5'] = ta.SMA(informative, timeperiod=5)
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# informative['sma10'] = ta.SMA(informative, timeperiod=10)
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# # informative['adx'] = ta.ADX(informative)
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#
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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, "1h", 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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for decalage in range(self.buy_decalage.value - 2, self.buy_decalage.value):
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conditions = [
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# (dataframe['bb_width'].shift(decalage) >= self.buy_bollinger.value),
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(dataframe['close'].shift(decalage) < dataframe['min200_001'].shift(decalage)),
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(dataframe['min_max200'] >= self.buy_min_max_n.value),
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# (dataframe['close'].shift(decalage) < dataframe['bb_lowerband'].shift(decalage)),
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# (dataframe['msma10_3'] > 0.998),
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# (dataframe['msma10_3'] > 0.999)
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(dataframe['rsi_1h'] > self.buy_rsi_min.value),
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(dataframe['rsi_1h'] < self.buy_rsi_max.value),
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]
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# GUARDS AND TRENDS
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if conditions:
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dataframe.loc[
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(
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(reduce(lambda x, y: x & y, conditions))
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)
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,
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['buy', 'buy_tag']] = (1, 'buy_msma1_' + str(decalage))
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break
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return dataframe
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def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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# dataframe.loc[
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# (
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# (dataframe['close'] < dataframe['open']) &
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# (dataframe['close'].shift(1) < dataframe['open'].shift(1)) &
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# (dataframe['close'].shift(2) < dataframe['open'].shift(2)) &
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# (dataframe['close'] < dataframe['bb_lowerband']) &
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# (((dataframe['bb_lowerband'].shift(2) - dataframe['bb_lowerband']) / dataframe['bb_lowerband']) >= 0.02)
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# # (((dataframe['close'].shift(1) - dataframe['close']) / dataframe['close']) >= 0.025)
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# ), 'sell'] = 1
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return dataframe
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