157 lines
5.1 KiB
Python
157 lines
5.1 KiB
Python
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# --- Do not remove these libs ---
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from freqtrade.strategy.interface import IStrategy
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from typing import Dict, List
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from functools import reduce
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from pandas import DataFrame
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# --------------------------------
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import talib.abstract as ta
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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import numpy # noqa
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class Examplestrategy5(IStrategy):
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"""
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Strategy 005
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author@: Gerald Lonlas
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github@: https://github.com/freqtrade/freqtrade-strategies
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How to use it?
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> python3 ./freqtrade/main.py -s Strategy005
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"""
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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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"1440": 0.01,
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"80": 0.02,
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"40": 0.03,
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"20": 0.04,
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"0": 0.05
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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 = -0.10
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# Optimal ticker interval for the strategy
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ticker_interval = '5m'
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# trailing stoploss
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trailing_stop = False
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trailing_stop_positive = 0.01
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trailing_stop_positive_offset = 0.02
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# run "populate_indicators" only for new candle
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process_only_new_candles = False
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# Experimental settings (configuration will overide these if set)
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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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# 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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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 []
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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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"""
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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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# Minus Directional Indicator / Movement
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dataframe['minus_di'] = ta.MINUS_DI(dataframe)
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# RSI
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dataframe['rsi'] = ta.RSI(dataframe)
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# Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
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rsi = 0.1 * (dataframe['rsi'] - 50)
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dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1)
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# Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy)
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dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
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# Stoch fast
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stoch_fast = ta.STOCHF(dataframe)
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dataframe['fastd'] = stoch_fast['fastd']
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dataframe['fastk'] = stoch_fast['fastk']
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# Overlap Studies
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# ------------------------------------
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# SAR Parabol
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dataframe['sar'] = ta.SAR(dataframe)
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# SMA - Simple Moving Average
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dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
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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
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:return: DataFrame with buy column
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"""
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dataframe.loc[
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# Prod
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(
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(dataframe['close'] > 0.00000200) &
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(dataframe['volume'] > dataframe['volume'].rolling(200).mean() * 4) &
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(dataframe['close'] < dataframe['sma']) &
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(dataframe['fastd'] > dataframe['fastk']) &
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(dataframe['rsi'] > 0) &
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(dataframe['fastd'] > 0) &
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# (dataframe['fisher_rsi'] < -0.94)
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(dataframe['fisher_rsi_norma'] < 38.900000000000006)
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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
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:return: DataFrame with buy column
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"""
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dataframe.loc[
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# Prod
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(
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(qtpylib.crossed_above(dataframe['rsi'], 50)) &
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(dataframe['macd'] < 0) &
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(dataframe['minus_di'] > 0)
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) |
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(
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(dataframe['sar'] > dataframe['close']) &
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(dataframe['fisher_rsi'] > 0.3)
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),
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'sell'] = 1
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return dataframe
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