# source: https://raw.githubusercontent.com/hxp-plus/freqtrade-strategy/b9bdb91f5944cca7429bd33452dae8fc458fbf6a/user_data/strategies/naive_strategy.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_hxp_plus_freqtrade_strategy__naive_strategy__20241110_065558(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 = 5 # 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": 0.01, "60": 0.02, "30": 0.03, "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 = "1m" # 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 buy_rsi = IntParameter( low=1, high=50, default=30, space="buy", optimize=True, load=True ) sell_rsi = IntParameter( low=50, high=100, default=70, space="sell", optimize=True, load=True ) short_rsi = IntParameter( low=51, high=100, default=70, space="sell", optimize=True, load=True ) exit_short_rsi = IntParameter( low=1, high=50, default=30, space="buy", optimize=True, load=True ) # 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: # RSI dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) # 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"] # Calculate OBV dataframe["obv"] = ta.OBV(dataframe) # Calculate VWAP dataframe["vwap"] = ( dataframe["volume"] * (dataframe["high"] + dataframe["low"] + dataframe["close"]) / 3 ).cumsum() / dataframe["volume"].cumsum() return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe["macd"] > dataframe["macdsignal"]) & (dataframe["rsi"] < 70) & (dataframe["close"] < dataframe["bb_upperband"]) & (dataframe["fastk"] < 80) & (dataframe["macdhist"] > 0) & (dataframe["close"] > dataframe["vwap"]) & (dataframe["obv"] > dataframe["obv"].shift(1)) ), "enter_long", ] = 1 dataframe.loc[ ( (dataframe["macd"] < dataframe["macdsignal"]) & (dataframe["rsi"] > 30) & (dataframe["close"] > dataframe["bb_lowerband"]) & (dataframe["fastk"] > 20) & (dataframe["macdhist"] < 0) & (dataframe["close"] < dataframe["vwap"]) & (dataframe["obv"] < dataframe["obv"].shift(1)) ), "enter_short", ] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe["macd"] < dataframe["macdsignal"]) & (dataframe["rsi"] > 70) & (dataframe["close"] > dataframe["bb_upperband"]) & (dataframe["fastk"] > 80) & (dataframe["macdhist"] < 0) & (dataframe["close"] < dataframe["vwap"]) & (dataframe["obv"] < dataframe["obv"].shift(1)) ), "exit_long", ] = 1 dataframe.loc[ ( (dataframe["macd"] > dataframe["macdsignal"]) & (dataframe["rsi"] < 30) & (dataframe["close"] < dataframe["bb_lowerband"]) & (dataframe["fastk"] < 20) & (dataframe["macdhist"] > 0) & (dataframe["close"] > dataframe["vwap"]) & (dataframe["obv"] > dataframe["obv"].shift(1)) ), "exit_short", ] = 1 return dataframe