289 lines
11 KiB
Python
289 lines
11 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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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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# This class is a sample. Feel free to customize it.
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class StrategyPierrick4112(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=0.095, space="buy")
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buy_bollinger_enabled = BooleanParameter(default=True, space="buy")
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# Valeur de la deuxième condition bollinger avec condition sma200
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buy_bollinger_2 = DecimalParameter(0.0, 0.08, decimals=2, default=0.04, space="buy")
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buy_bollinger_2_enabled = BooleanParameter(default=True, 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=0.098, space="buy")
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buy_sma_percent_enabled = BooleanParameter(default=True, space="buy")
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# volume à atteindre
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buy_volume = IntParameter(0, 50, default=0, space="buy")
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buy_volume_enabled = BooleanParameter(default=True, space="buy")
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buy_candel_percent = DecimalParameter(1.02, 1.10, decimals=2, default=1.04, space="buy")
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# buy_rsi = IntParameter(20, 40, default=30, space="buy")
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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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'sma100': {'color': 'green'},
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'sma500': {'color': 'blue'},
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'sma200_98': {'color': 'yellow'},
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'sma200_95': {'color': 'cyan'},
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'rsi': {'color': '#c58893'}
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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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"Pct": {
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'percent': {'color': 'white'}
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}
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}
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}
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def informative_pairs(self):
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return []
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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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# # 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['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['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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dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100)
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dataframe["percent"] = (dataframe["close"] - dataframe["open"]) / dataframe["open"]
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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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# 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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#
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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['bb_lower_reg'] = dataframe["bb_lowerband"] - dataframe["bb_lowerband"].shift(1)
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return dataframe
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def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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value = 0
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p = dataframe['close'].shift(20) / dataframe['close']
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for k, v in p.iteritems():
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# print(k, v)
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value = v
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condition1 = np.where(value >= 1.04, True, False)
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conditions = []
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# GUARDS AND TRENDS
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if self.buy_bollinger_enabled.value:
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conditions.append(dataframe['bb_width'] >= self.buy_bollinger.value)
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conditions2 = []
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if self.buy_bollinger_2_enabled.value:
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conditions2.append(dataframe['bb_width'] >= self.buy_bollinger_2.value)
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conditions_volume = []
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condition_volume = True
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if self.buy_volume_enabled.value:
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conditions_volume.append(dataframe['volume'] >= self.buy_volume.value * 1000)
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if conditions_volume:
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condition_volume = np.where(conditions_volume, True, False)
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condition2 = False
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if conditions2:
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condition2 = reduce(lambda x, y: x & y, conditions2) & condition1
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condition_sma = False
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conditions_sma = []
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if self.buy_sma_percent_enabled.value:
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# conditions_sma.append(dataframe['close'] <= dataframe['sma200'] * self.buy_sma_percent.value)
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conditions_sma.append((dataframe['sma100'].shift(36) - dataframe['sma100']) / dataframe[
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'sma100'] > self.buy_sma_percent.value)
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if conditions_sma:
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condition_sma = reduce(lambda x, y: x & y, conditions_sma)
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if conditions:
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dataframe.loc[
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(
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# (((dataframe['close'].shift(1) - dataframe['close']) / dataframe['close']) < 0.025) &
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(dataframe['close'] < dataframe['bb_lowerband']) &
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condition_volume &
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(reduce(lambda x, y: x & y, conditions)) #| (condition2 & condition_sma))
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) | (
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# condition2 &
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(dataframe['close'] > dataframe['bb_upperband']) &
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(dataframe['close'] > dataframe['open'] * 1.04) &
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# (dataframe['bb_width'] < 0.2)
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(dataframe['sma100'].shift(4) < dataframe['sma100'] * 1.01) &
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(dataframe['sma100'].shift(4) > dataframe['sma100'] * 0.99)
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# ) | (
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# (
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# (dataframe['percent']
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# + dataframe['percent'].shift(1)
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# + dataframe['percent'].shift(2)
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# + dataframe['percent'].shift(3)
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# + dataframe['percent'].shift(4) > 1.04) &
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# (dataframe['close'] > dataframe['bb_upperband'])
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# ) & (
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# dataframe['close'] > dataframe['open'].shift(2) * 1.04
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# )
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)
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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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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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),
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'sell'] = 1
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return dataframe
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