198 lines
8.0 KiB
Python
198 lines
8.0 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, STOCHRSI, STOCH, STOCHF, RSI
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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_ethusdt_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 = 1
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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": 0.07186329732926479,
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"6": 0.03610260437996321,
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"14": 0.014117594921808408,
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"23": 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 = -0.073946396013718
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# Trailing stoploss
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trailing_stop = True
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trailing_stop_positive = 0.11645094244761
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trailing_stop_positive_offset = 0.20201226976340847
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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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# 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 = True
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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': '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': 'gtc',
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'sell': 'gtc'
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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 [("ETH/USDT", "1m")]
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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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# MACD
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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['macd_angle'] = LINEARREG_ANGLE(dataframe['macd'], timeperiod=3)
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dataframe['macdhist_angle'] = LINEARREG_ANGLE(dataframe['macd'], timeperiod=3)
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# Linear angle
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dataframe['angle'] = LINEARREG_ANGLE(dataframe['close'], timeperiod=14)
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dataframe['tema'] = TEMA(dataframe['close'], timeperiod=30)
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dataframe['sr_fastk'], dataframe['sr_fastd'] = STOCHRSI(dataframe['close'], timeperiod=14, fastk_period=5,
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fastd_period=3, fastd_matype=0)
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dataframe['sr_fastd_angle'] = LINEARREG_ANGLE(dataframe['sr_fastd'], timeperiod=4)
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dataframe['slowk'], dataframe['slowd'] = STOCH(dataframe['high'], dataframe['low'], dataframe['close'],
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fastk_period=5, slowk_period=3, slowk_matype=0, slowd_period=3,
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slowd_matype=0)
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dataframe['slowd_angle'] = LINEARREG_ANGLE(dataframe['slowd'], timeperiod=3)
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dataframe['sf_fastk'], dataframe['sf_fastd'] = STOCHF(dataframe['high'], dataframe['low'], dataframe['close'], fastk_period=5, fastd_period=3, fastd_matype=0)
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dataframe['sf_fastd_angle'] = LINEARREG_ANGLE(dataframe['sf_fastd'], timeperiod=3)
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dataframe['rsi'] = RSI(dataframe['close'], timeperiod=14)
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dataframe['rsi_angle'] = LINEARREG_ANGLE(dataframe['rsi'], timeperiod=5)
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# # first check if dataprovider is available
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# if self.dp:
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# if self.dp.runmode in ('live', 'dry_run'):
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# ob = self.dp.orderbook(metadata['pair'], 1)
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# dataframe['best_bid'] = ob['bids'][0][0]
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# dataframe['best_ask'] = ob['asks'][0][0]
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#
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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['macd'], dataframe['macdsignal'])) &
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((dataframe['macd_angle']) > -0.007579827857282747) &
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(dataframe['tema'] < 0.124417394428206) &
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(dataframe['sr_fastd_angle'] > -39) &
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(dataframe['sf_fastk'] > 33) &
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(dataframe['rsi_angle'] > 24) &
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(dataframe['volume'] > 0) # Make sure Volume is not 0
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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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((dataframe['macdhist']) < 0.000241872676925719) &
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(dataframe['sr_fastd_angle'] > 71) &
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(dataframe['sf_fastd_angle'] < 21) &
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(dataframe['rsi_angle'] < 1) &
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(dataframe['volume'] > 0) # Make sure Volume is not 0
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),
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'sell'] = 1
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return dataframe
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