283 lines
12 KiB
Python
283 lines
12 KiB
Python
# pragma 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 pandas import DataFrame
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from talib._ta_lib import ULTOSC, MACD, SAR, LINEARREG_ANGLE, TEMA, TSF, CCI, ATR, CORREL, \
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BOP, WMA, KAMA, HT_DCPERIOD, HT_TRENDMODE, HT_SINE
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from freqtrade.strategy import merge_informative_pair
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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 quick_btcusdt_1m(IStrategy):
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"""
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This is a sample strategy to inspire you.
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More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md
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You can:
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:return: a Dataframe with all mandatory indicators for the strategies
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- Rename the class name (Do not forget to update class_name)
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- Add any methods you want to build your strategy
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- Add any lib you need to build your strategy
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You must keep:
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- the lib in the section "Do not remove these libs"
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- the prototype for the methods: minimal_roi, stoploss, populate_indicators, populate_buy_trend,
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populate_sell_trend, hyperopt_space, buy_strategy_generator
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"""
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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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# Minimal ROI designed for the strategy.
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# This attribute will be overridden if the config file contains "minimal_roi".
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minimal_roi = {
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"0": 0.06443,
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"360": 0.06597,
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"1790": 0.0108,
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"2116": 0
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}
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# Optimal stoploss designed for the strategy.
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# This attribute will be overridden if the config file contains "stoploss".
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# Stoploss:
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stoploss = -0.15825
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# Trailing stop:
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trailing_stop = True
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trailing_stop_positive = 0.3274
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trailing_stop_positive_offset = 0.38967
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trailing_only_offset_is_reached = True
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# Optimal ticker interval for the strategy.
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timeframe = '1m'
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# Run "populate_indicators()" only for new candle.
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process_only_new_candles = 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': 'market',
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'sell': 'market',
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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': 'fok',
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'sell': 'fok'
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}
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plot_config = {
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'main_plot': {
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'close': {},
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'sar': {'color': 'white'},
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},
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'subplots': {
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"MACD": {
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'macd': {'color': 'blue'},
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'macdsignal': {'color': 'orange'},
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},
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"OU": {
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'ou': {'color': 'red'},
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}
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}
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}
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def informative_pairs(self):
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"""
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Define additional, informative pair/interval combinations to be cached from the exchange.
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These pair/interval combinations are non-tradeable, unless they are part
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of the whitelist as well.
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For more information, please consult the documentation
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:return: List of tuples in the format (pair, interval)
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Sample: return [("ETH/USDT", "5m"),
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("BTC/USDT", "15m"),
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]
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"""
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return [("BTC/USDT", "1h")]
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Adds several different TA indicators to the given DataFrame
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Performance Note: For the best performance be frugal on the number of indicators
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you are using. Let uncomment only the indicator you are using in your strategies
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or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
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:param dataframe: Dataframe with data from the exchange
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:param metadata: Additional information, like the currently traded pair
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:return: a Dataframe with all mandatory indicators for the strategies
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"""
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dataframe['period'] = HT_DCPERIOD(dataframe['close'])
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dataframe['macd'], dataframe['macdsignal'], dataframe['macdhist'] = MACD(dataframe['close'], fastperiod=12,
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slowperiod=26, signalperiod=9)
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dataframe['cci'] = CCI(dataframe['high'], dataframe['low'], dataframe['close'], timeperiod=30)
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dataframe['sar'] = SAR(dataframe['high'], dataframe['low'])
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dataframe['wma'] = WMA(dataframe['close'], timeperiod=30)
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dataframe['wma_ratio'] = (dataframe['close'] - dataframe['wma'])
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dataframe['kama'] = KAMA(dataframe['close'], timeperiod=30)
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dataframe['angle_kama'] = LINEARREG_ANGLE(dataframe['kama'], timeperiod=10)
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dataframe['tsf_mid'] = TSF(dataframe['close'], timeperiod=30)
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dataframe['angle_tsf_mid'] = LINEARREG_ANGLE(dataframe['tsf_mid'], timeperiod=10)
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dataframe['atr'] = ATR(dataframe['high'], dataframe['low'], dataframe['close'], timeperiod=30)
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dataframe['uo'] = ULTOSC(dataframe['high'], dataframe['low'], dataframe['close'], timeperiod1=7, timeperiod2=14,
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timeperiod3=28)
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dataframe['tema'] = TEMA(dataframe['close'], timeperiod=50)
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dataframe['macd_ratio'] = (dataframe['macd'] - dataframe['macdsignal'])
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dataframe['tsf_ratio'] = (dataframe['tsf_mid'] - dataframe['close'])
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dataframe['correl_h_l'] = CORREL(dataframe['high'], dataframe['low'], timeperiod=30)
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dataframe['correl_tsf_mid_close'] = CORREL(dataframe['tsf_mid'], dataframe['close'], timeperiod=12)
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dataframe['bop'] = BOP(dataframe['open'], dataframe['high'], dataframe['low'], dataframe['close'])
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if not self.dp:
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# Don't do anything if DataProvider is not available.
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return dataframe
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# Get the informative pair
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informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='1h')
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informative['bop'] = BOP(informative['open'], informative['high'], informative['low'], informative['close'])
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informative['period'] = HT_DCPERIOD(informative['close'])
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informative['mode'] = HT_TRENDMODE(informative['close'])
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informative['sine'], informative['leadsine'] = HT_SINE(informative['close'])
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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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"""
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Based on TA indicators, populates the buy signal for the given dataframe
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:param dataframe: DataFrame populated with indicators
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:param metadata: Additional information, like the currently traded pair
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:return: DataFrame with buy column
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"""
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dataframe.loc[
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(
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(qtpylib.crossed_above(dataframe['low'], dataframe['tsf_mid']))
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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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Based on TA indicators, populates the sell signal for the given dataframe
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:param dataframe: DataFrame populated with indicators
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:param metadata: Additional information, like the currently traded pair
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:return: DataFrame with buy column
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"""
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dataframe.loc[
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(
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(qtpylib.crossed_below(dataframe['high'], dataframe['tsf_mid']))
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),
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'sell'] = 1
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return dataframe
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"""
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freqtrade hyperopt --config user_data/config_btcusdt_1m.json --hyperopt hyper_btcusdt_1m --hyperopt-loss OnlyProfitHyperOptLoss --strategy quick_btcusdt_1m -e 500 --spaces all
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+--------+---------+----------+------------------+--------------+-------------------------------+----------------+-------------+
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| Best | Epoch | Trades | Win Draw Loss | Avg profit | Profit | Avg duration | Objective |
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|--------+---------+----------+------------------+--------------+-------------------------------+----------------+-------------|
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| * Best | 1/500 | 2 | 1 0 1 | -3.39% | -67.10930706 USDT (-6.77%) | 4,154.0 m | 1.02257 |
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| * Best | 2/500 | 2 | 1 0 1 | 0.08% | 1.60699817 USDT (0.16%) | 1,156.5 m | 0.99946 |
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| * Best | 8/500 | 4 | 4 0 0 | 3.59% | 142.17731149 USDT (14.35%) | 981.5 m | 0.95218 |
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| Best | 39/500 | 13 | 11 0 2 | 3.80% | 489.61841267 USDT (49.41%) | 736.2 m | 0.83531 |
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| Best | 70/500 | 25 | 19 4 2 | 2.99% | 740.67168932 USDT (74.74%) | 1,597.7 m | 0.75086 |
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| Best | 106/500 | 52 | 35 12 5 | 1.82% | 937.66182441 USDT (94.62%) | 1,666.9 m | 0.6846 |
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| Best | 427/500 | 52 | 32 17 3 | 2.83% | 1,460.67464976 USDT (147.40%) | 2,592.9 m | 0.50868 |
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| Best | 439/500 | 179 | 101 57 21 | 0.83% | 1,480.44117660 USDT (149.39%) | 3,457.9 m | 0.50203 |
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[Epoch 500 of 500 (100%)] || | [Time: 1:22:09, Elapsed Time: 1:22:09]
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2021-03-26 22:31:08,615 - freqtrade.optimize.hyperopt - INFO - 500 epochs saved to '/home/crypto_rahino/freqtrade/user_data/hyperopt_results/strategy_quick_btcusdt_1m_hyperopt_results_2021-03-26_21-08-16.pickle'.
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Best result:
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439/500: 179 trades. 101/57/21 Wins/Draws/Losses. Avg profit 0.83%. Median profit 0.82%. Total profit 1480.44117660 USDT ( 149.39Σ%). Avg duration 3457.9 min. Objective: 0.50203
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# Buy hyperspace params:
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buy_params = {
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'angle_tsf_mid-enabled': False,
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'angle_tsf_mid-value': 9,
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'atr-enabled': False,
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'atr-value': 180,
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'bop-value': 0.7274,
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'cci-enabled': False,
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'cci-value': 57,
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'correl_h_l-enabled': True,
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'correl_h_l-value': -0.5389,
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'correl_tsf_mid_close-enabled': False,
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'correl_tsf_mid_close-value': -0.8364,
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'macd_ratio-enabled': False,
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'macd_ratio-value': 101,
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'macdhist-enabled': True,
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'macdhist-value': 22,
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'macdsignal-enabled': False,
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'macdsignal-value': -478,
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'trigger': 'macd',
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'tsf_ratio-enabled': False,
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'tsf_ratio-value': -1323,
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'uo-enabled': False,
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'uo-value': 33.5021
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}
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# Sell hyperspace params:
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sell_params = {
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'angle_tsf_mid-enabled': False,
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'angle_tsf_mid-value': 9,
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'atr-enabled': False,
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'atr-value': 180,
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'cci-enabled': False,
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'cci-value': 57,
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'correl_h_l-enabled': True,
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'correl_h_l-value': -0.5389,
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'correl_tsf_mid_close-enabled': False,
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'correl_tsf_mid_close-value': -0.8364,
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'macd_ratio-enabled': False,
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'macd_ratio-value': 101,
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'macdhist-enabled': True,
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'macdhist-value': 22,
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'macdsignal-enabled': False,
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'macdsignal-value': -478,
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'trigger': 'macd',
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'tsf_ratio-enabled': False,
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'tsf_ratio-value': -1323,
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'uo-enabled': False,
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'uo-value': 33.5021
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}
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# ROI table:
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minimal_roi = {
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"0": 0.16344,
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"793": 0.05931,
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"1121": 0.03143,
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"1474": 0
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}
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# Stoploss:
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stoploss = -0.2884
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# Trailing stop:
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trailing_stop = True
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trailing_stop_positive = 0.21554
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trailing_stop_positive_offset = 0.23749
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trailing_only_offset_is_reached = False
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""" |