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Jérôme Delacotte
272
StrategyPierrick2.py
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272
StrategyPierrick2.py
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# 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 functools import reduce
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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
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from pandas import DataFrame
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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 StrategyPierrick2(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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# ROI table:
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minimal_roi = {
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"0": 1,
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# "600": 0.12,
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# "1200": 0.08,
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# "2400": 0.06,
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# "3600": 0.04,
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# "7289": 0
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}
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# Stoploss:
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stoploss = -1
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# Buy hypers
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timeframe = '4h'
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# Trailing stoploss
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trailing_stop = False
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trailing_stop_positive = 0.15
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trailing_stop_positive_offset = 0.20
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trailing_only_offset_is_reached = True
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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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},
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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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"Aaron": {
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'aroonup': {'color': 'blue'},
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'aroondown': {'color': 'red'}
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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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previous_last_candle = dataframe.iloc[-2].squeeze()
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previous_5_candle = dataframe.iloc[-5].squeeze()
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#print("last_candle", last_candle)
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#print("previous_last_candle", previous_last_candle)
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count = 0
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for coin, balance in self.wallets.get_all_balances().items():
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count = count + 1
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# print(coin, " ", balance)
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# print("count=", count)
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# (last_candle['percent5'] < -0.005) \
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# if (0 < current_profit < 0.005) \
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# & ((current_time - trade.open_date_utc).seconds >= 3600 * 2):
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# # & (previous_last_candle['sma10'] > last_candle['sma10']):
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# print("too_small_gain", pair, trade, " profit=", current_profit, " rate=", current_rate, " percent5=",
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# last_candle['percent5'])
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# return 'too_small_gain'
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# if (current_profit < -0.05) \
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# & ((current_time - trade.open_date_utc).days >= 3):
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# print("lost_half_profit", pair, trade, " profit=", current_profit, " rate=", current_rate)
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# return 'stop_loss_profit'
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# if (current_profit > 0.02) \
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# & (last_candle['percent'] < 0.01) \
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# & ((current_time - trade.open_date_utc).seconds >= 3600):
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# print("lost_half_profit", pair, trade, " profit=", current_profit, " rate=", current_rate)
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# return 'lost_half_profit'
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# ((current_time - trade.open_date_utc).seconds >= 3600 * 2) \
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if (current_profit > 0) \
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& (previous_5_candle['sma10'] > last_candle['sma10'] * 1.005) \
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& ((last_candle['percent'] < 0) & (last_candle['percent3'] < - (current_profit / 4))):
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# print("over_bb_band_sma10_desc", pair, trade, " profit=", current_profit, " rate=", current_rate)
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return 'over_bb_band_sma10_desc'
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# if (current_profit > 0) \
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# & (last_candle['percent'] < -0.02):
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# # print("over_bb_band_sma10_desc", pair, trade, " profit=", current_profit, " rate=", current_rate)
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# return 'stop_percent_loss'
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#if (current_profit > 0) \
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# & ((current_time - trade.open_date_utc).seconds >= 3600 * 2) \
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# & (previous_last_candle['sma20'] > last_candle['sma20']) \
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# & (last_candle['percent'] < 0):
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# print("over_bb_band_sma20_desc", pair, trade, " profit=", current_profit, " rate=", current_rate)
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# return 'over_bb_band_sma20_desc'
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if (current_profit > 0) \
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& (last_candle['rsi'] > 88): #| (previous_last_candle['rsi'] > 75 & last_candle['rsi'] < 70)):
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# print("over_rsi", pair, trade, " profit=", current_profit, " rate=", current_rate)
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return 'over_rsi'
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# description trade
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# Trade(id=0, pair=CAKE/USDT, amount=4.19815281, open_rate=11.91000000, open_since=2021-12-22 17:55:00)
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# print(last_candle)
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# if 0.05 < current_profit < 1:
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# if (
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# (previous_last_candle['sma10'] > last_candle['sma10'] * 1.005) &
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# (current_time - trade.open_date_utc).seconds >= 3600 * 3
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# # ) | (
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# # (current_time - trade.open_date_utc).seconds >= 3600 * 6
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# ):
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# # self.lock_pair(pair, until=current_time + timedelta(hours=3))
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#
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# print("profit_3h_sma10_desc", pair, trade, " profit=", current_profit, " rate=", current_rate)
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# return 'profit_3h_sma10_desc'
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#
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# if (0 < current_profit < 0.1) \
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# & (previous_last_candle['sma20'] > last_candle['sma20']) \
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# & ((current_time - trade.open_date_utc).seconds >= 3600 * 5):
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# print("profit_5h_sma20_desc", pair, trade, " profit=", current_profit, " rate=", current_rate)
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# return 'profit_5h_sma20_desc'
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# if (count == self.config['max_open_trades']) & (current_profit < -0.04) \
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# & ((current_time - trade.open_date_utc).seconds >= 3600 * 6):
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# self.lock_pair(pair, until=current_time + timedelta(hours=10))
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# print("stop_short_loss", pair, trade, " profit=", current_profit, " rate=", current_rate,
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# "count=", count, "max=", self.config['max_open_trades'])
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# return 'stop_short_loss'
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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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#
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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['sma20'] = ta.SMA(dataframe, timeperiod=20)
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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["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["percent20"] = dataframe["percent"].rolling(20).sum()
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dataframe['min'] = ta.MIN(dataframe['close'], timeperiod=200)
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dataframe['min20'] = ta.MIN(dataframe['close'], timeperiod=20)
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dataframe['max'] = ta.MAX(dataframe['close'], timeperiod=200)
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dataframe['max_min'] = dataframe['max'] / dataframe['min']
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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['bb_min'] = ta.MIN(dataframe['bb_lowerband'], timeperiod=36)
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# Bollinger Bands - Weighted (EMA based instead of SMA)
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weighted_bollinger = qtpylib.weighted_bollinger_bands(
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qtpylib.typical_price(dataframe), window=20, stds=2
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)
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dataframe["wbb_upperband"] = weighted_bollinger["upper"]
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dataframe["wbb_lowerband"] = weighted_bollinger["lower"]
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dataframe["wbb_middleband"] = weighted_bollinger["mid"]
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dataframe["wbb_percent"] = (
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(dataframe["close"] - dataframe["wbb_lowerband"]) /
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(dataframe["wbb_upperband"] - dataframe["wbb_lowerband"])
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)
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dataframe["wbb_width"] = (
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(dataframe["wbb_upperband"] - dataframe["wbb_lowerband"]) / dataframe["wbb_middleband"]
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)
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# # EMA - Exponential Moving Average
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# dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
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return dataframe
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def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe.loc[
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(
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(
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(dataframe['close'] < dataframe['bb_lowerband'])
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& (dataframe['bb_width'] >= 0.065)
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#& (dataframe['rsi'] < 45)
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& (dataframe['volume'] * dataframe['close'] / 1000 >= 100)
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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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),
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'sell'] = 1
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return dataframe
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