# source: https://raw.githubusercontent.com/wisarmy/exbot/0ace43a62549d69d9253ba972bb83c4e75d0c29c/exbot-py/strategies/ichiv1.py import datetime import os from pandas import DataFrame import pytz import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib import pandas as pd from strategies.istrategy import IStrategy pd.options.mode.chained_assignment = None # default='warn' import technical.indicators as ftt from functools import reduce class github_wisarmy_exbot__ichiv1__20230918_072444(IStrategy): # NOTE: settings as of the 25th july 21 # Buy hyperspace params: buy_params = { "buy_trend_above_senkou_level": 1, "buy_trend_bullish_level": 6, "buy_fan_magnitude_shift_value": 3, "buy_min_fan_magnitude_gain": 1.001 # "buy_min_fan_magnitude_gain": 1.002 # NOTE: Good value (Win% ~70%), alot of trades # "buy_min_fan_magnitude_gain": 1.008, # NOTE: Very save value (Win% ~90%), only the biggest moves 1.008, } # Sell hyperspace params: # NOTE: was 15m but kept bailing out in dryrun sell_params = { "sell_trend_indicator": "trend_close_2h", } # ROI table: minimal_roi = {"0": 0.059, "10": 0.037, "41": 0.012, "114": 0} # Stoploss: stoploss = -0.275 # Optimal timeframe for the strategy timeframe = "5m" startup_candle_count = 96 process_only_new_candles = False trailing_stop = False # trailing_stop_positive = 0.002 # trailing_stop_positive_offset = 0.025 # trailing_only_offset_is_reached = True use_sell_signal = True sell_profit_only = False ignore_roi_if_buy_signal = False plot_config = { "main_plot": { # fill area between senkou_a and senkou_b "senkou_a": { "color": "green", # optional "fill_to": "senkou_b", "fill_label": "Ichimoku Cloud", # optional "fill_color": "rgba(255,76,46,0.2)", # optional }, # plot senkou_b, too. Not only the area to it. "senkou_b": {}, "trend_close_5m": {"color": "#FF5733"}, "trend_close_15m": {"color": "#FF8333"}, "trend_close_30m": {"color": "#FFB533"}, "trend_close_1h": {"color": "#FFE633"}, "trend_close_2h": {"color": "#E3FF33"}, "trend_close_4h": {"color": "#C4FF33"}, "trend_close_6h": {"color": "#61FF33"}, "trend_close_8h": {"color": "#33FF7D"}, }, "subplots": { "fan_magnitude": {"fan_magnitude": {}}, "fan_magnitude_gain": {"fan_magnitude_gain": {}}, }, } def populate_indicators(self, dataframe: DataFrame) -> DataFrame: dataframe = dataframe.reset_index() heikinashi = qtpylib.heikinashi(dataframe) dataframe["open"] = heikinashi["open"] # dataframe['close'] = heikinashi['close'] dataframe["high"] = heikinashi["high"] dataframe["low"] = heikinashi["low"] dataframe = dataframe.set_index("date") dataframe["trend_close_5m"] = dataframe["close"] dataframe["trend_close_15m"] = ta.EMA(dataframe["close"], timeperiod=3) dataframe["trend_close_30m"] = ta.EMA(dataframe["close"], timeperiod=6) dataframe["trend_close_1h"] = ta.EMA(dataframe["close"], timeperiod=12) dataframe["trend_close_2h"] = ta.EMA(dataframe["close"], timeperiod=24) dataframe["trend_close_4h"] = ta.EMA(dataframe["close"], timeperiod=48) dataframe["trend_close_6h"] = ta.EMA(dataframe["close"], timeperiod=72) dataframe["trend_close_8h"] = ta.EMA(dataframe["close"], timeperiod=96) dataframe["trend_open_5m"] = dataframe["open"] dataframe["trend_open_15m"] = ta.EMA(dataframe["open"], timeperiod=3) dataframe["trend_open_30m"] = ta.EMA(dataframe["open"], timeperiod=6) dataframe["trend_open_1h"] = ta.EMA(dataframe["open"], timeperiod=12) dataframe["trend_open_2h"] = ta.EMA(dataframe["open"], timeperiod=24) dataframe["trend_open_4h"] = ta.EMA(dataframe["open"], timeperiod=48) dataframe["trend_open_6h"] = ta.EMA(dataframe["open"], timeperiod=72) dataframe["trend_open_8h"] = ta.EMA(dataframe["open"], timeperiod=96) dataframe["fan_magnitude"] = ( dataframe["trend_close_1h"] / dataframe["trend_close_8h"] ) dataframe["fan_magnitude_gain"] = dataframe["fan_magnitude"] / dataframe[ "fan_magnitude" ].shift(1) ichimoku = ftt.ichimoku( dataframe, conversion_line_period=20, base_line_periods=60, laggin_span=120, displacement=30, ) dataframe["chikou_span"] = ichimoku["chikou_span"] dataframe["tenkan_sen"] = ichimoku["tenkan_sen"] dataframe["kijun_sen"] = ichimoku["kijun_sen"] dataframe["senkou_a"] = ichimoku["senkou_span_a"] dataframe["senkou_b"] = ichimoku["senkou_span_b"] dataframe["leading_senkou_span_a"] = ichimoku["leading_senkou_span_a"] dataframe["leading_senkou_span_b"] = ichimoku["leading_senkou_span_b"] dataframe["cloud_green"] = ichimoku["cloud_green"] dataframe["cloud_red"] = ichimoku["cloud_red"] dataframe["atr"] = ta.ATR(dataframe) return dataframe def condition_early_close_seconds(self, df, conditions): condition_early_close_seconds = int( os.getenv("CONDITION_EARLY_CLOSE_SECONDS", 0) ) timeframe_seconds = df.index.unique().to_series().diff().min().total_seconds() threshold = condition_early_close_seconds last_date = df.index[-1] next_date = last_date + datetime.timedelta(seconds=timeframe_seconds) now_date = datetime.datetime.now(tz=pytz.timezone("Asia/Shanghai")) # early close of last candle if next_date - now_date > datetime.timedelta(seconds=threshold): conditions.append(df.index < last_date) df.loc[df.index >= last_date, "signal_by"] = pd.Series( "removed_by_early_close", index=df.index[df.index >= last_date], ) def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame: conditions = [] self.condition_early_close_seconds(dataframe, conditions) # Trending market if self.buy_params["buy_trend_above_senkou_level"] >= 1: conditions.append(dataframe["trend_close_5m"] > dataframe["senkou_a"]) conditions.append(dataframe["trend_close_5m"] > dataframe["senkou_b"]) if self.buy_params["buy_trend_above_senkou_level"] >= 2: conditions.append(dataframe["trend_close_15m"] > dataframe["senkou_a"]) conditions.append(dataframe["trend_close_15m"] > dataframe["senkou_b"]) if self.buy_params["buy_trend_above_senkou_level"] >= 3: conditions.append(dataframe["trend_close_30m"] > dataframe["senkou_a"]) conditions.append(dataframe["trend_close_30m"] > dataframe["senkou_b"]) if self.buy_params["buy_trend_above_senkou_level"] >= 4: conditions.append(dataframe["trend_close_1h"] > dataframe["senkou_a"]) conditions.append(dataframe["trend_close_1h"] > dataframe["senkou_b"]) if self.buy_params["buy_trend_above_senkou_level"] >= 5: conditions.append(dataframe["trend_close_2h"] > dataframe["senkou_a"]) conditions.append(dataframe["trend_close_2h"] > dataframe["senkou_b"]) if self.buy_params["buy_trend_above_senkou_level"] >= 6: conditions.append(dataframe["trend_close_4h"] > dataframe["senkou_a"]) conditions.append(dataframe["trend_close_4h"] > dataframe["senkou_b"]) if self.buy_params["buy_trend_above_senkou_level"] >= 7: conditions.append(dataframe["trend_close_6h"] > dataframe["senkou_a"]) conditions.append(dataframe["trend_close_6h"] > dataframe["senkou_b"]) if self.buy_params["buy_trend_above_senkou_level"] >= 8: conditions.append(dataframe["trend_close_8h"] > dataframe["senkou_a"]) conditions.append(dataframe["trend_close_8h"] > dataframe["senkou_b"]) # Trends bullish if self.buy_params["buy_trend_bullish_level"] >= 1: conditions.append(dataframe["trend_close_5m"] > dataframe["trend_open_5m"]) if self.buy_params["buy_trend_bullish_level"] >= 2: conditions.append( dataframe["trend_close_15m"] > dataframe["trend_open_15m"] ) if self.buy_params["buy_trend_bullish_level"] >= 3: conditions.append( dataframe["trend_close_30m"] > dataframe["trend_open_30m"] ) if self.buy_params["buy_trend_bullish_level"] >= 4: conditions.append(dataframe["trend_close_1h"] > dataframe["trend_open_1h"]) if self.buy_params["buy_trend_bullish_level"] >= 5: conditions.append(dataframe["trend_close_2h"] > dataframe["trend_open_2h"]) if self.buy_params["buy_trend_bullish_level"] >= 6: conditions.append(dataframe["trend_close_4h"] > dataframe["trend_open_4h"]) if self.buy_params["buy_trend_bullish_level"] >= 7: conditions.append(dataframe["trend_close_6h"] > dataframe["trend_open_6h"]) if self.buy_params["buy_trend_bullish_level"] >= 8: conditions.append(dataframe["trend_close_8h"] > dataframe["trend_open_8h"]) # Trends magnitude conditions.append( dataframe["fan_magnitude_gain"] >= self.buy_params["buy_min_fan_magnitude_gain"] ) conditions.append(dataframe["fan_magnitude"] > 1) for x in range(self.buy_params["buy_fan_magnitude_shift_value"]): conditions.append( dataframe["fan_magnitude"].shift(x + 1) < dataframe["fan_magnitude"] ) if conditions: dataframe.loc[reduce(lambda x, y: x & y, conditions), "buy"] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame: conditions = [] self.condition_early_close_seconds(dataframe, conditions) conditions.append( qtpylib.crossed_below( dataframe["trend_close_5m"], dataframe[self.sell_params["sell_trend_indicator"]], ) ) if conditions: dataframe.loc[reduce(lambda x, y: x & y, conditions), "sell"] = 1 return dataframe def populate_close_position(self, df: DataFrame) -> DataFrame: df["take_profit"] = pd.Series(dtype="str") df["stop_loss"] = pd.Series(dtype="str") return df def run(self, df, ex, args): df = self.populate_indicators(df) df = self.populate_buy_trend(df) df = self.populate_sell_trend(df) df = self.populate_close_position(df) self.trade(ex, df, args) return df