144 lines
5.1 KiB
Django/Jinja
144 lines
5.1 KiB
Django/Jinja
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
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# flake8: noqa: F401
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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 freqtrade.strategy import IStrategy
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from freqtrade.strategy import CategoricalParameter, DecimalParameter, IntParameter
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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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class {{ strategy }}(IStrategy):
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"""
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This is a strategy template to get you started.
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More information in https://www.freqtrade.io/en/latest/strategy-customization/
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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 methods: populate_indicators, populate_buy_trend, populate_sell_trend
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You should keep:
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- timeframe, minimal_roi, stoploss, trailing_*
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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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"60": 0.01,
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"30": 0.02,
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"0": 0.04
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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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# Trailing stoploss
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trailing_stop = False
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# trailing_only_offset_is_reached = False
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# trailing_stop_positive = 0.01
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# trailing_stop_positive_offset = 0.0 # Disabled / not configured
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# Optimal timeframe for the strategy.
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timeframe = '5m'
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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 | indent(4) }}
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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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: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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{{ indicators | indent(8) }}
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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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{{ buy_trend | indent(16) }}
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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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{{ sell_trend | indent(16) }}
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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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{{ additional_methods | indent(4) }}
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