# source: https://raw.githubusercontent.com/hamidreza07/freqai-strategy/5bd42947b5300922a342d153182906e7cdbe8a40/classic/kijun_cross_strong_s.py 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_hamidreza07_freqai_strategy__kijun_cross_strong_s__20231226_132626(IStrategy): # 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 # Proposed timeframe for the strategy. Can be altered to your own preferred timeframe. timeframe = "1d" # Can this strategy go short? can_short: bool = False # Minimal ROI designed for the strategy. # Set to 10000% since the exit signal determines the trade exit. # Some crypto even got ROI triggered at 100% so had to set it to this value. minimal_roi = {"0": 100.0} # Optimal stoploss designed for the strategy. # Set to 100% since the exit signal dermines the trade exit. stoploss = -1.0 # Trailing stoploss trailing_stop = False # 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 # Set to the default of 30. startup_candle_count: int = 30 # 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"} @property def plot_config(self): return { "main_plot": { "kijun": {"color": "blue"}, 'senkou_a': { 'color': 'green', 'fill_to': 'senkou_b', 'fill_label': 'Ichimoku Cloud', 'fill_color': 'rgba(255,76,46,0.2)', }, 'senkou_b': {} } } def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # CREATE ICHIMOKU INDICATOR # Specify the lenghts for each indicator (20, 60, 120, 60 is for crypto trading) TS = 9 KS = 26 SS = 52 CS = 26 OS = 0 # Each column represents the output of the First Ichimoku Variable tuple and its specific column. dataframe["tenkan"] = pta.ichimoku( high=dataframe["high"], low=dataframe["low"], close=dataframe["close"], tenkan=TS, kijun=KS, senkou=SS, offset=OS, )[0][f"ITS_{TS}"] dataframe["kijun"] = pta.ichimoku( high=dataframe["high"], low=dataframe["low"], close=dataframe["close"], tenkan=TS, kijun=KS, senkou=SS, offset=OS, )[0][f"IKS_{KS}"] dataframe["senkou_a"] = pta.ichimoku( high=dataframe["high"], low=dataframe["low"], close=dataframe["close"], tenkan=TS, kijun=KS, senkou=SS, offset=OS, )[0][f"ISA_{TS}"] dataframe["senkou_b"] = pta.ichimoku( high=dataframe["high"], low=dataframe["low"], close=dataframe["close"], tenkan=TS, kijun=KS, senkou=SS, offset=OS, )[0][f"ISB_{KS}"] dataframe["chikou"] = pta.ichimoku( high=dataframe["high"], low=dataframe["low"], close=dataframe["close"], tenkan=TS, kijun=KS, senkou=SS, offset=OS, )[0][f"ICS_{KS}"] # Buy long singals are enhanced by explanations # Kijun should be above senkou_a AND kijun should be above senkou_b to make the signal TRUE dataframe["kijun_above_cloud"] = (dataframe["kijun"] > dataframe["senkou_a"]) & (dataframe["kijun"] > dataframe["senkou_b"]) # Close price should also be above senkou_a AND senkou_b to let the signal be TRUE dataframe["close_above_cloud"] = (dataframe["close"] > dataframe["senkou_a"]) & (dataframe["close"] > dataframe["senkou_b"]) # The third part of the total buy signal is when the close price is above the kijun dataframe["close_above_kijun"] = dataframe["close"] > dataframe["kijun"] # The buy long signal can only be True if the kijun_above_kumo, close_above_kumo and close_above_kijun are all True dataframe["buy_long"] = ( (dataframe["kijun_above_cloud"] == True) & (dataframe["close_above_cloud"] == True) & (dataframe["close_above_kijun"] == True) ) # The sell short signals are similar to buy long, but reversed dataframe["kijun_below_cloud"] = (dataframe["kijun"] < dataframe["senkou_a"]) & (dataframe["kijun"] < dataframe["senkou_b"]) dataframe["close_below_cloud"] = (dataframe["close"] < dataframe["senkou_a"]) & (dataframe["close"] < dataframe["senkou_b"]) dataframe["close_below_kijun"] = dataframe["close"] < dataframe["kijun"] dataframe["sell_short"] = ( (dataframe["kijun_below_cloud"] == True) & (dataframe["close_below_cloud"] == True) & (dataframe["close_below_kijun"] == True) ) # 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] # print(self) print(metadata) # print(dataframe) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( # If buy long signal is True, then enter a long trade (dataframe["buy_long"] == True) & (dataframe["volume"] > 0) # Guard ), ["enter_long", "enter_tag"], ] = (1, "Strong_long_signal") # For short trades, use the section below dataframe.loc[ ( # If sell short signal is True, then enter a short trade (dataframe["sell_short"] == True) & (dataframe["volume"] > 0) # Guard ), ["enter_short", "enter_tag"], ] = (1, "Strong_short_signal") return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( # The exit signal for long trades is pretty straightforward. # Sell when the close price is below the kijun sen (dataframe["close"] < dataframe["kijun"]) & (dataframe["volume"] > 0) # Guard ), ["exit_long", "exit_tag"], ] = (1, "Close_below_kijun") # For short trades, use the section below dataframe.loc[ ( # The exit signal for shorts trades is pretty straightforward. # Sell when the close price is above the kijun sen (dataframe["close"] > dataframe["kijun"]) & (dataframe["volume"] > 0) # Guard ), ["exit_short", "exit_tag"], ] = (1, "Close_above_kijun") return dataframe