freqtrade_origin/freqtrade/templates/base_strategy.py.j2

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# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# --- Do not remove these libs ---
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import numpy as np # noqa
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import pandas as pd # noqa
from pandas import DataFrame
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from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
IStrategy, IntParameter)
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# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import pandas_ta as pta
import freqtrade.vendor.qtpylib.indicators as qtpylib
class {{ strategy }}(IStrategy):
"""
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This is a strategy template to get you started.
More information in https://www.freqtrade.io/en/latest/strategy-customization/
You can:
:return: a Dataframe with all mandatory indicators for the strategies
- Rename the class name (Do not forget to update class_name)
- Add any methods you want to build your strategy
- Add any lib you need to build your strategy
You must keep:
- the lib in the section "Do not remove these libs"
- the methods: populate_indicators, populate_entry_trend, populate_exit_trend
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You should keep:
- timeframe, minimal_roi, stoploss, trailing_*
"""
# Strategy interface version - allow new iterations of the strategy interface.
# Check the documentation or the Sample strategy to get the latest version.
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INTERFACE_VERSION = 3
# Optimal timeframe for the strategy.
timeframe = '5m'
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# Can this strategy go short?
can_short: bool = False
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi".
minimal_roi = {
"60": 0.01,
"30": 0.02,
"0": 0.04
}
# Optimal stoploss designed for the strategy.
# This attribute will be overridden if the config file contains "stoploss".
stoploss = -0.10
# Trailing stoploss
trailing_stop = False
# trailing_only_offset_is_reached = False
# trailing_stop_positive = 0.01
# trailing_stop_positive_offset = 0.0 # Disabled / not configured
# Run "populate_indicators()" only for new candle.
process_only_new_candles = False
# These values can be overridden in the "ask_strategy" section in the config.
use_sell_signal = True
sell_profit_only = False
ignore_roi_if_buy_signal = False
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 30
# Strategy parameters
buy_rsi = IntParameter(10, 40, default=30, space="buy")
sell_rsi = IntParameter(60, 90, default=70, space="sell")
# Optional order type mapping.
order_types = {
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'entry': 'limit',
'exit': 'limit',
'stoploss': 'market',
'stoploss_on_exchange': False
}
# Optional order time in force.
order_time_in_force = {
'entry': 'gtc',
'exit': 'gtc'
}
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{{ plot_config | indent(4) }}
def informative_pairs(self):
"""
Define additional, informative pair/interval combinations to be cached from the exchange.
These pair/interval combinations are non-tradeable, unless they are part
of the whitelist as well.
For more information, please consult the documentation
:return: List of tuples in the format (pair, interval)
Sample: return [("ETH/USDT", "5m"),
("BTC/USDT", "15m"),
]
"""
return []
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
Performance Note: For the best performance be frugal on the number of indicators
you are using. Let uncomment only the indicator you are using in your strategies
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
:param dataframe: Dataframe with data from the exchange
:param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
"""
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{{ indicators | indent(8) }}
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the entry signal for the given dataframe
:param dataframe: DataFrame
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with entry columns populated
"""
dataframe.loc[
(
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{{ buy_trend | indent(16) }}
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
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'enter_long'] = 1
# Uncomment to use shorts (Only used in futures/margin mode. Check the documentation for more info)
"""
dataframe.loc[
(
{{ sell_trend | indent(16) }}
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'enter_short'] = 1
"""
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the exit signal for the given dataframe
:param dataframe: DataFrame
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with exit columns populated
"""
dataframe.loc[
(
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{{ sell_trend | indent(16) }}
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
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'exit_long'] = 1
# Uncomment to use shorts (Only used in futures/margin mode. Check the documentation for more info)
"""
dataframe.loc[
(
{{ buy_trend | indent(16) }}
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'exit_short'] = 1
"""
return dataframe
{{ additional_methods | indent(4) }}