281 lines
11 KiB
Python
281 lines
11 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
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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 strg_ETHBTC_1m2(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": 0.04025819697656752,
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"7": 0.015188707936204564,
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"18": 0.005472487470606337,
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"41": 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 = -0.33515742514178193
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# Trailing stoploss
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trailing_stop = False
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trailing_stop_positive = 0.34089
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trailing_stop_positive_offset = 0.43254
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trailing_only_offset_is_reached = False
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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 = 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': {
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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/BTC", "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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# # Ultimate Oscillator
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dataframe['uo'] = ULTOSC(dataframe['high'], dataframe['low'], dataframe['close'], timeperiod1=7, timeperiod2=14, timeperiod3=28)
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# MACD
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dataframe['macd'], dataframe['macdsignal'], dataframe['macdhist'] = MACD(dataframe['close'], fastperiod=12, slowperiod=26, signalperiod=9)
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# Parabolic SAR
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dataframe['sar'] = SAR(dataframe['high'], dataframe['low'], acceleration=0, maximum=0)
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# Linear angle
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dataframe['angle'] = LINEARREG_ANGLE(dataframe['close'], timeperiod=14)
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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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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['close'], dataframe['sar'])) &
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(0 > (dataframe['sar'] - dataframe['close'])) &
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(1 > (dataframe['sar'] - dataframe['sar'].shift(3))) &
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(1 > dataframe['macd']) &
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(1 > dataframe['macdhist']) &
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(0 < (dataframe['macdhist'] - dataframe['macdhist'].shift(3))) &
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(0 < dataframe['angle']) &
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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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(qtpylib.crossed_below(dataframe['sar'], dataframe['close'])) &
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(dataframe['uo'] < 69) &
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(0 > (dataframe['sar'] - dataframe['close'])) &
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(1 > (dataframe['sar'] - dataframe['sar'].shift(3))) &
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(0 < dataframe['macd']) &
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(1 < dataframe['macdhist']) &
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(0 > (dataframe['macdhist'] - dataframe['macdhist'].shift(3))) &
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(0 > dataframe['angle']) &
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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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"""
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buy1:
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trigger = macd cross above macdsignal
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guard = sar < close
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guard = 70 > ou > 50
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guard = lenear_angle > 0
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buy2:
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trigger = sar < close
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guard = macd > macdsignal
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guard = 70 > ou > 50
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buy3:
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trigger = ou cross below 30 & close > max(close)
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sell1:
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trigger = macd cross below macdsignal
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guard = sar > close
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guard = 50 > ou > 20
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sell2:
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trigger = sar > close
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guard = macd < macdsignal
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guard = 50 > ou > 20
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"""
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"""
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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 | 3/500 | 1194 | 523 654 17 | -0.12% | -0.06906927 BTC (-138.00%) | 1,559.8 m | 2.71708 |
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| * Best | 6/500 | 100 | 20 0 80 | -0.07% | -0.00352831 BTC (-7.05%) | 11.5 m | 1.87067 |
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| Best | 37/500 | 12 | 6 0 6 | 0.49% | 0.00294367 BTC (5.88%) | 18.2 m | 1.86009 |
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| Best | 67/500 | 10 | 6 0 4 | 0.89% | 0.00443786 BTC (8.87%) | 19.5 m | 1.85245 |
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| Best | 91/500 | 73 | 21 5 47 | 0.00% | 0.00007645 BTC (0.15%) | 10.2 m | 1.85215 |
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| Best | 92/500 | 48 | 17 2 29 | 0.05% | 0.00116911 BTC (2.34%) | 10.4 m | 1.85189 |
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| Best | 94/500 | 12 | 6 0 6 | 0.69% | 0.00416071 BTC (8.31%) | 17.8 m | 1.85143 |
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| Best | 110/500 | 18 | 6 1 11 | 0.36% | 0.00327838 BTC (6.55%) | 14.3 m | 1.85113 |
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| Best | 257/500 | 48 | 16 0 32 | 0.06% | 0.00154456 BTC (3.09%) | 11.3 m | 1.8505 |
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| Best | 388/500 | 74 | 23 14 37 | 0.01% | 0.00045826 BTC (0.92%) | 8.2 m | 1.84665 |
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[Epoch 500 of 500 (100%)] || | [Time: 1:04:49, Elapsed Time: 1:04:49]
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2021-01-31 01:14:47,851 - freqtrade.optimize.hyperopt - INFO - 500 epochs saved to '/home/yakov/PycharmProjects/freqtrade/.env/bin/user_data/hyperopt_results/hyperopt_results_2021-01-31_00-07-23.pickle'.
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Best result:
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388/500: 74 trades. 23/14/37 Wins/Draws/Losses. Avg profit 0.01%. Median profit -0.02%. Total profit 0.00045826 BTC ( 0.92Σ%). Avg duration 8.2 min. Objective: 1.84665
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# Buy hyperspace params:
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buy_params = {
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'angle-enabled': False,
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'macd-enabled': False,
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'macd_value': 0.73579,
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'macdhist_shift': 0.87895,
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'macdhist_value': 0.29935,
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'sar-enabled': False,
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'sar_ratio': 0.43819,
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'sar_shift': 0.98992,
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'trigger': 'sell-macd_cross_signal'
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}
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# Sell hyperspace params:
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sell_params = {
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'angle_h_s': 0.07105,
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'macd_value_s': 0.08408,
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'macdhist_shift_s': -0.72567,
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'macdhist_value_s': 0.77324,
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'sar_ratio_s': 0.58347,
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'sar_shift_s': 0.81212,
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'sell-angle-enabled': True,
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'sell-macd-enabled': False,
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'sell-sar-enabled': False,
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'sell-uo-enabled': True,
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'trigger': 'sell-macd_cross_signal',
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'uo_l_s': 14
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}
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# ROI table:
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minimal_roi = {
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"0": 0.07193,
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"3": 0.0382,
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"5": 0.01183,
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"7": 0
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}
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# Stoploss:
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stoploss = -0.25471
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# Trailing stop:
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trailing_stop = True
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trailing_stop_positive = 0.04697
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trailing_stop_positive_offset = 0.05329
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trailing_only_offset_is_reached = True
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""" |