# source: https://raw.githubusercontent.com/imadev26/crypto-trading-bot/4335d4bd136c42854e81def5ee01afa882707de2/user_data/strategies/TrendMasterPro.py # directory_url: https://github.com/imadev26/crypto-trading-bot/blob/main/user_data/strategies/ # User: imadev26 # Repository: crypto-trading-bot # --------------------# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # flake8: noqa: F401 # isort: skip_file import numpy as np import pandas as pd from datetime import datetime, timedelta, timezone from pandas import DataFrame from typing import Optional, Union, Dict from freqtrade.strategy import ( IStrategy, Trade, Order, PairLocks, informative, BooleanParameter, CategoricalParameter, DecimalParameter, IntParameter, RealParameter, ) import talib.abstract as ta import pandas_ta as pta from technical import qtpylib class Github_imadev26_crypto_trading_bot__TrendMasterPro__20260831_150440(IStrategy): """ ================================================================================ Github_imadev26_crypto_trading_bot__TrendMasterPro__20260831_150440 - Institutional Multi-Timeframe Trend Following Strategy ================================================================================ Designed for High Win-rate, strict Risk Management, and Capital Preservation. Key Mechanisms: 1. Macro 1h Higher Timeframe Trend Filter: - 1h EMA 50 > 1h EMA 200 (Macro Golden Cross) - 1h Close > 1h EMA 200 (Above major support) - 1h ADX > 20 (Strong market trend confirmation) 2. Micro 5m Entry Triggers: - 5m EMA 9 > 5m EMA 21 (Short-term bullish momentum) - 5m RSI Pullback into value zone (40 - 65) - 5m Volume Spike (Volume > 1.2x Volume SMA 20) - Price above 5m EMA 50 3. Institutional Exits & Risk Protection: - Hard Stoploss: -4.0% - Trailing Stop: Activates at +2.5% profit, trails by 1.2% - Dynamic ROI Take-Profit Ladder - Custom Indicator Exit (EMA breakdown or RSI > 75 overbought reversal) """ INTERFACE_VERSION = 3 # Base Timeframe timeframe = "5m" informative_timeframe = "1h" # Spot (can_short = False) or Futures (can_short = True) can_short: bool = False # Startup candles to warm up 1h EMA 200 (200 * 12 = 2400 5m candles or 200 1h candles) startup_candle_count: int = 250 # Process only new candles for speed and clean signals process_only_new_candles = True # ------------------------------------------------------------------------- # Risk Management & ROI # ------------------------------------------------------------------------- # Hard Stoploss (-4%) stoploss = -0.04 # Trailing Stoploss (Locks in profit when price pumps) trailing_stop = True trailing_stop_positive = 0.012 # 1.2% trailing distance trailing_stop_positive_offset = 0.025 # Activates once +2.5% profit is reached trailing_only_offset_is_reached = True # Dynamic ROI Ladder (Takes profit automatically based on trade duration) minimal_roi = { "0": 0.05, # 5.0% profit immediate exit "30": 0.03, # 3.0% profit after 30 mins "60": 0.02, # 2.0% profit after 60 mins "120": 0.012 # 1.2% profit after 2 hours } # Exit Signals use_exit_signal = True exit_profit_only = True # Exit signals trigger only if trade is in profit ignore_roi_if_entry_signal = False # Order Types order_types = { "entry": "limit", "exit": "limit", "emergency_exit": "market", "force_entry": "market", "force_exit": "market", "stoploss": "market", "stoploss_on_exchange": False } order_time_in_force = { "entry": "GTC", "exit": "GTC" } # Protections (Modern Freqtrade style) protections = [ { "method": "CooldownPeriod", "stop_duration_candles": 5 }, { "method": "StoplossGuard", "lookback_period_candles": 60, "trade_limit": 2, "stop_duration_candles": 60, "only_per_pair": True }, { "method": "MaxDrawdown", "lookback_period_candles": 240, "trade_limit": 4, "stop_duration_candles": 120, "max_allowed_drawdown": 0.10 } ] # ------------------------------------------------------------------------- # Hyperoptable Parameters (Can be tuned with 'freqtrade hyperopt') # ------------------------------------------------------------------------- # Buy / Entry Parameters buy_rsi_min = IntParameter(30, 50, default=40, space="buy", optimize=True) buy_rsi_max = IntParameter(55, 75, default=65, space="buy", optimize=True) buy_volume_multiplier = DecimalParameter(1.0, 2.5, default=1.2, decimals=1, space="buy", optimize=True) buy_adx_1h_min = IntParameter(15, 35, default=20, space="buy", optimize=True) # Sell / Exit Parameters sell_rsi = IntParameter(70, 85, default=75, space="sell", optimize=True) # ------------------------------------------------------------------------- # Informative Higher Timeframe (1 Hour) Indicators # ------------------------------------------------------------------------- @informative("1h") def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Macro EMAs dataframe["ema_50"] = ta.EMA(dataframe, timeperiod=50) dataframe["ema_200"] = ta.EMA(dataframe, timeperiod=200) # 1h RSI & ADX (Trend Strength) dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) dataframe["adx"] = ta.ADX(dataframe, timeperiod=14) return dataframe # ------------------------------------------------------------------------- # Base Timeframe (5 Min) Indicators # ------------------------------------------------------------------------- def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Moving Averages (5m) dataframe["ema_9"] = ta.EMA(dataframe, timeperiod=9) dataframe["ema_21"] = ta.EMA(dataframe, timeperiod=21) dataframe["ema_50"] = ta.EMA(dataframe, timeperiod=50) dataframe["ema_200"] = ta.EMA(dataframe, timeperiod=200) # Momentum & Oscillators dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) # Volume Moving Average dataframe["volume_mean_20"] = dataframe["volume"].rolling(window=20).mean() # 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"]) ) # Average True Range (ATR) for volatility dataframe["atr"] = ta.ATR(dataframe, timeperiod=14) return dataframe # ------------------------------------------------------------------------- # Entry Signals (Long) # ------------------------------------------------------------------------- def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] # 1. Macro 1h Trend Conditions (Market Alignment) conditions.append(dataframe["ema_50_1h"] > dataframe["ema_200_1h"]) # Macro Golden Cross conditions.append(dataframe["close_1h"] > dataframe["ema_200_1h"]) # Price above Macro 200 EMA conditions.append(dataframe["adx_1h"] >= self.buy_adx_1h_min.value) # Macro Trend is strong # 2. Micro 5m Trend Conditions (Local Momentum) conditions.append(dataframe["ema_9"] > dataframe["ema_21"]) # Fast EMA cross conditions.append(dataframe["close"] > dataframe["ema_50"]) # Price above 50 EMA # 3. Pullback / Value Zone Trigger (Avoid buying overbought peaks) conditions.append(dataframe["rsi"] >= self.buy_rsi_min.value) conditions.append(dataframe["rsi"] <= self.buy_rsi_max.value) # 4. Volume Spike Confirmation (Institutional backing) conditions.append( dataframe["volume"] > (dataframe["volume_mean_20"] * self.buy_volume_multiplier.value) ) # 5. Volume sanity check conditions.append(dataframe["volume"] > 0) if conditions: dataframe.loc[ np.logical_and.reduce(conditions), "enter_long" ] = 1 return dataframe # ------------------------------------------------------------------------- # Exit Signals (Sell / Take-Profit / Trend Breakdown) # ------------------------------------------------------------------------- def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] # 1. Overbought RSI Reversal Signal rsi_overbought = (dataframe["rsi"] > self.sell_rsi.value) & ( qtpylib.crossed_below(dataframe["rsi"], self.sell_rsi.value) ) # 2. Fast EMA Breakdown (EMA 9 crossed below EMA 21) ema_breakdown = qtpylib.crossed_below(dataframe["ema_9"], dataframe["ema_21"]) conditions.append(rsi_overbought | ema_breakdown) conditions.append(dataframe["volume"] > 0) if conditions: dataframe.loc[ np.logical_and.reduce(conditions), "exit_long" ] = 1 return dataframe