# source: https://raw.githubusercontent.com/Chris6622/class-AdvancedFuturesSwingStrategy-IStrategy-/786cfa2c323feab11c072706b06cb9e4243af820/user_data/strategies/BBSTOMACDStrategy.py # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # flake8: noqa: F401 # isort: skip_file # --- Do not remove these imports --- import numpy as np import pandas as pd from datetime import datetime, timedelta, timezone from pandas import DataFrame from typing import Optional, Union from freqtrade.strategy import ( IStrategy, Trade, Order, PairLocks, informative, # @informative decorator # Hyperopt Parameters BooleanParameter, CategoricalParameter, DecimalParameter, IntParameter, RealParameter, # timeframe helpers timeframe_to_minutes, timeframe_to_next_date, timeframe_to_prev_date, # Strategy helper functions merge_informative_pair, stoploss_from_absolute, stoploss_from_open, ) # -------------------------------- # Add your lib to import here import talib.abstract as ta from technical import qtpylib # This class is a sample. Feel free to customize it. class Github_Chris6622_class_AdvancedFuturesSwingStrategy_IStrategy___BBSTOMACDStrategy__20260208_084829(IStrategy): """ This is a sample strategy to inspire you. 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 # 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 = { # "120": 0.0, # exit after 120 minutes at break even "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 # Optimal timeframe for the strategy. timeframe = "3m" # 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 # Hyperoptable parameters for buying bb_window = IntParameter(10, 50, default=20, space='buy', optimize=True) # Bollinger Bands window bb_stds = RealParameter(1.5, 3.0, default=2.0, space='buy', optimize=True) # Bollinger Bands std dev stoch_rsi_period = IntParameter(5, 20, default=14, space='buy', optimize=True) # Stochastic RSI period obv_ma_window = IntParameter(5, 50, default=12, space='buy', optimize=True) # OBV moving average period # Hyperoptable parameters for selling sell_rsi = IntParameter(40, 70, default=60, space='sell', optimize=True) # Sell RSI threshold sell_macd_cross = CategoricalParameter([True, False], default=True, space='sell', optimize=True) # Sell when MACD crosses sell_bb_percent = RealParameter(0.0, 1.0, default=0.8, space='sell', optimize=True) # Sell when price is near upper BB # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 200 # Optional order type mapping. order_types = { "entry": "limit", "exit": "limit", "stoploss": "market", "stoploss_on_exchange": False, } # Optional order time in force. order_time_in_force = {"entry": "GTC", "exit": "GTC"} plot_config = { "main_plot": { "tema": {}, "sar": {"color": "white"}, }, "subplots": { "MACD": { "macd": {"color": "blue"}, "macdsignal": {"color": "orange"}, }, "RSI": { "rsi": {"color": "red"}, }, }, } 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 """ # # Stochastic Slow # stoch = ta.STOCH(dataframe) # dataframe['slowd'] = stoch['slowd'] # dataframe['slowk'] = stoch['slowk'] # Stochastic Fast stoch_fast = ta.STOCHF(dataframe) dataframe["fastd"] = stoch_fast["fastd"] dataframe["fastk"] = stoch_fast["fastk"] # MACD macd = ta.MACD(dataframe) dataframe["macd"] = macd["macd"] dataframe["macdsignal"] = macd["macdsignal"] dataframe["macdhist"] = macd["macdhist"] # Bollinger Bands bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe["bb_lowerband"] = bollinger["lower"] dataframe["bb_middleband"] = bollinger["mid"] dataframe["bb_upperband"] = bollinger["upper"] dataframe["bb_percent"] = (dataframe["close"] - dataframe["bb_lowerband"]) / ( dataframe["bb_upperband"] - dataframe["bb_lowerband"] ) dataframe["bb_width"] = (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe[ "bb_middleband"] # OBV dataframe["obv"] = ta.OBV(dataframe) dataframe["obv_ma_12"] = dataframe["obv"].rolling(window=12).mean() # Retrieve best bid and best ask from the orderbook # ------------------------------------ """ # first check if dataprovider is available if self.dp: if self.dp.runmode.value in ('live', 'dry_run'): ob = self.dp.orderbook(metadata['pair'], 1) dataframe['best_bid'] = ob['bids'][0][0] dataframe['best_ask'] = ob['asks'][0][0] """ 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[ ( (dataframe["close"] < dataframe["bb_lowerband"]) & # Price below lower BB (qtpylib.crossed_above(dataframe["macd"], dataframe["macdsignal"])) & # MACD cross up (qtpylib.crossed_above(dataframe["fastk"], dataframe["fastd"])) & # Stochastic cross up (dataframe["fastk"] < 20) & # Stochastic in oversold (dataframe["obv"] > dataframe["obv_ma_12"]) # OBV confirms up move ), "enter_long", ] = 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[ ( (dataframe["close"] > dataframe["bb_upperband"]) | # Price above upper BB (qtpylib.crossed_below(dataframe["macd"], dataframe["macdsignal"])) | # MACD cross down ( (qtpylib.crossed_below(dataframe["fastk"], dataframe["fastd"])) & (dataframe["fastk"] > 80) # Stochastic cross down in overbought ) | (dataframe["obv"] < dataframe["obv_ma_12"]) # OBV weakens ), "exit_long", ] = 1 return dataframe