# source: https://raw.githubusercontent.com/qninhdt/lmao/30875f9329ca8f2cae9f67551a28addf91ca8d22/user_data/strategies/ichiv1_plus.py # --- Do not remove these libs --- from freqtrade.strategy.interface import IStrategy from pandas import DataFrame import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib import pandas as pd # noqa import numpy as np pd.options.mode.chained_assignment = None # default='warn' import technical.indicators as ftt from functools import reduce from datetime import datetime, timedelta from freqtrade.strategy import merge_informative_pair from freqtrade.strategy import stoploss_from_open class Github_qninhdt_lmao__ichiv1_plus__20250917_165523(IStrategy): # can_short = True # 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": 1.002, # 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: # Enhanced sell parameter configuration sell_params = { # Basic trend indicators "sell_trend_indicator": "trend_close_2h", "sell_short_trend": "trend_close_5m", # Ranging market filter parameters "adx_threshold": 25, # ADX threshold, below this value is considered ranging market "bb_width_percentile": 30, # Bollinger band width percentile threshold # Confirmation indicator thresholds "rsi_overbought": 70, # RSI overbought threshold "volume_confirmation": 1.2, # Volume confirmation multiplier "trend_consistency_min": 0.3, # Minimum trend consistency value # Tiered sell thresholds "partial_sell_ratio": 0.4, # Partial sell ratio "strong_sell_confirmation": 3, # Strong sell signal confirmation count } # ROI table: # minimal_roi = { # "0": 0.059, # "10": 0.037, # "41": 0.012, # "115": 0 # } minimal_roi = { "0": 0.03, # Immediate pump, 3% take profit "60": 0.02, # After 1 hour, 2% can exit "240": 0.01, # After 4 hours, 1% can exit "720": 0, # After 12 hours, breakeven exit } # Stoploss: stoploss = -0.255 # Optimal timeframe for the strategy timeframe = "15m" startup_candle_count = 96 process_only_new_candles = True trailing_stop = True trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.03 trailing_only_offset_is_reached = True use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_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": {}}, }, } # Fixed leverage mode: directly use constant multiplier fixed_leverage: float = 2.0 def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: heikinashi = qtpylib.heikinashi(dataframe) dataframe["open"] = heikinashi["open"] # dataframe['close'] = heikinashi['close'] dataframe["high"] = heikinashi["high"] dataframe["low"] = heikinashi["low"] 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) # Ranging market identification indicators dataframe["adx"] = ta.ADX(dataframe) dataframe["atr"] = ta.ATR(dataframe) dataframe["atr_pct"] = (dataframe["atr"] / dataframe["close"]) * 100 # Bollinger bands for volatility analysis bollinger = qtpylib.bollinger_bands(dataframe["close"], window=20, stds=2) dataframe["bb_upper"] = bollinger["upper"] dataframe["bb_lower"] = bollinger["lower"] dataframe["bb_width"] = ( (dataframe["bb_upper"] - dataframe["bb_lower"]) / dataframe["close"] ) * 100 # Trend consistency score (multi-timeframe trend direction consistency) trend_directions = [] timeframes = ["5m", "15m", "30m", "1h", "2h", "4h"] for tf in timeframes: trend_col = f"trend_close_{tf}" if trend_col in dataframe.columns: trend_directions.append( (dataframe[trend_col] > dataframe[trend_col].shift(1)).astype(int) ) if trend_directions: dataframe["trend_consistency"] = sum(trend_directions) / len( trend_directions ) else: dataframe["trend_consistency"] = 0.5 # RSI for overbought confirmation dataframe["rsi"] = ta.RSI(dataframe) # Volume related indicators dataframe["volume_sma"] = ta.SMA(dataframe["volume"], timeperiod=20) dataframe["volume_ratio"] = dataframe["volume"] / dataframe["volume_sma"] # Ranging market identification (ADX < 25 and small BB width) dataframe["is_ranging"] = (dataframe["adx"] < 25) & ( dataframe["bb_width"] < dataframe["bb_width"].rolling(50).quantile(0.3) ) 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 populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> 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_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Initialize sell signal column dataframe["sell"] = 0.0 # ============ Basic trend crossover conditions ============ basic_sell_signal = qtpylib.crossed_below( dataframe[self.sell_params["sell_short_trend"]], dataframe[self.sell_params["sell_trend_indicator"]], ) # ============ Confirmation indicators collection ============ confirmations = [] # 1. RSI overbought confirmation rsi_confirmation = dataframe["rsi"] > self.sell_params["rsi_overbought"] confirmations.append(rsi_confirmation) # 2. Volume confirmation (volume decline) volume_confirmation = ( dataframe["volume_ratio"] > self.sell_params["volume_confirmation"] ) confirmations.append(volume_confirmation) # 3. Ichimoku confirmation (price breaks below conversion line) ichimoku_confirmation = dataframe["close"] < dataframe["tenkan_sen"] confirmations.append(ichimoku_confirmation) # 4. Trend consistency deterioration confirmation trend_deterioration = ( dataframe["trend_consistency"] < self.sell_params["trend_consistency_min"] ) confirmations.append(trend_deterioration) # 5. Cloud break confirmation cloud_break = (dataframe["close"] < dataframe["senkou_a"]) & ( dataframe["close"] < dataframe["senkou_b"] ) confirmations.append(cloud_break) # Calculate confirmation signal count confirmation_count = sum([conf.astype(int) for conf in confirmations]) # ============ Ranging market protection mechanism ============ # Increase sell threshold in ranging markets to reduce frequent trading ranging_market = dataframe["is_ranging"] # ============ Tiered sell logic ============ # Partial sell conditions (only partial sell in ranging markets) partial_sell_conditions = ( basic_sell_signal & (confirmation_count >= 1) & ranging_market & (dataframe["adx"] < self.sell_params["adx_threshold"]) ) # Strong sell conditions (trending market or multiple confirmations) strong_sell_conditions = basic_sell_signal & ( # Confirmed sell in trending market ((~ranging_market) & (confirmation_count >= 2)) | # Or strong sell with multiple confirmations (confirmation_count >= self.sell_params["strong_sell_confirmation"]) ) # Emergency sell conditions (multiple negative signals appearing simultaneously) emergency_sell_conditions = ( basic_sell_signal & (confirmation_count >= 4) & (dataframe["rsi"] > 75) # Severely overbought & cloud_break & ( dataframe["close"] < dataframe["bb_lower"] ) # Break below Bollinger lower band ) # ============ Apply sell signals ============ # Partial sell (40% position) dataframe.loc[partial_sell_conditions, "sell"] = self.sell_params[ "partial_sell_ratio" ] # Strong sell (70% position) dataframe.loc[strong_sell_conditions, "sell"] = 0.7 # Emergency full sell (100% position) dataframe.loc[emergency_sell_conditions, "sell"] = 1.0 # ============ Additional market environment adaptability adjustments ============ # If fan magnitude deteriorates sharply, enhance sell signal fan_deterioration = ( dataframe["fan_magnitude"] < 0.98 ) & ( # Short-term trend weaker than long-term trend dataframe["fan_magnitude_gain"] < 0.995 ) # And continues to deteriorate # Additional sell when fan deteriorates fan_sell_conditions = ( basic_sell_signal & fan_deterioration & (confirmation_count >= 1) ) dataframe.loc[fan_sell_conditions, "sell"] = np.maximum( dataframe["sell"], 0.6 # Sell at least 60% ) return dataframe # ============================================================= # Fixed leverage: only return set or config-overridden fixed_leverage # ------------------------------------------------------------- def leverage( self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, entry_tag: str, side: str, **kwargs, ) -> float: if hasattr(self, "config"): sp = self.config.get("strategy_parameters", {}) or {} cfg_val = sp.get("fixed_leverage") if cfg_val is not None: try: self.fixed_leverage = float(cfg_val) except Exception: pass return float(max(1.0, min(self.fixed_leverage, max_leverage)))