# source: https://raw.githubusercontent.com/nicnl31/pyalgotrader/c22b45dc6744b8dc3ea5c9e73858c81274686684/frameworks/freqtrade/user_data/strategies/KAMA.py # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # flake8: noqa: F401 # isort: skip_file # --- Do not remove these libs --- import numpy as np import pandas as pd from pandas import DataFrame from datetime import datetime from typing import Optional, Union from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, IntParameter, IStrategy, merge_informative_pair) # -------------------------------- # Add your lib to import here import talib.abstract as ta import pandas_ta as pta from technical import qtpylib class Github_nicnl31_pyalgotrader__KAMA__20240902_122600(IStrategy): """ 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 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. INTERFACE_VERSION = 3 # Optimal timeframe for the strategy. timeframe = '4h' # 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 = { "0": 0.3 } # Optimal stoploss designed for the strategy. # This attribute will be overridden if the config file contains "stoploss". stoploss = -0.1 # 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 = True # These values can be overridden in the config. use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 121 # Strategy parameters window_length = 21 num_stdev = 3 # Optional order type mapping. order_types = { 'entry': 'limit', 'exit': 'market', 'stoploss': 'market', 'stoploss_on_exchange': False } # Optional order time in force. order_time_in_force = { 'entry': 'GTC', 'exit': 'GTC' } @property def plot_config(self): return { # Main plot indicators (Moving averages, ...) 'main_plot': { 'kama': {'color': '#AB924F'} }, 'subplots': { # Subplots - each dict defines one additional plot "Github_nicnl31_pyalgotrader__KAMA__20240902_122600": { 'distance': {'color': '#4F58AB'}, f'distance_sma{self.window_length}': {'color': '#8DBA56'}, f'distance_sma{self.window_length}_upperband': { 'color': '#C2E09D'}, f'distance_sma{self.window_length}_lowerband': { 'color': '#42611D'} } } } 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 """ # Github_nicnl31_pyalgotrader__KAMA__20240902_122600100 dataframe['kama'] = ta.Github_nicnl31_pyalgotrader__KAMA__20240902_122600(dataframe, timeperiod=100) # Distance from the close price to the Github_nicnl31_pyalgotrader__KAMA__20240902_122600100 dataframe['distance'] = dataframe['close'] - dataframe['kama'] # The SMA of the distance dataframe[f'distance_sma{self.window_length}'] = ta.SMA( dataframe['distance'], timeperiod=self.window_length) # The rolling standard deviation of the distance dataframe['stdev'] = ta.STDDEV(dataframe['distance'], timeperiod=self.window_length) # Lower and upper bands of the SMA dataframe[f'distance_sma{self.window_length}_upperband'] = \ dataframe[f'distance_sma{self.window_length}'] + \ self.num_stdev * dataframe['stdev'] dataframe[f'distance_sma{self.window_length}_lowerband'] = \ dataframe[f'distance_sma{self.window_length}'] - \ self.num_stdev * dataframe['stdev'] 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[ ( (qtpylib.crossed_above( dataframe['distance'], dataframe[f'distance_sma{self.window_length}_lowerband']) ) & # Signal: distance crosses above lower band (dataframe['volume'] > 0) # Make sure Volume is not 0 ), 'enter_long'] = 1 dataframe.loc[ ( (qtpylib.crossed_below( dataframe['distance'], dataframe[ f'distance_sma{self.window_length}_upperband']) ) & # Signal: distance crosses below upper band (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[ ( (qtpylib.crossed_below( dataframe['close'], dataframe['kama'])) & # Signal: distance crosses below upper band (dataframe['volume'] > 0) # Make sure Volume is not 0 ), 'exit_long'] = 1 # Uncomment to use shorts (Only used in futures/margin mode. Check the documentation for more info) dataframe.loc[ ( (qtpylib.crossed_above( dataframe['close'], dataframe['kama'])) & # Signal: distance crosses below upper band (dataframe['volume'] > 0) # Make sure Volume is not 0 ), 'exit_short'] = 1 return dataframe