# source: https://raw.githubusercontent.com/ammaryaser99/crypto-trading-bot/834e27500d5b01600541bd44bf500d294f7932c8/user_data/strategies/AggressiveMomentumScalper.py """Aggressive, long-only spot momentum strategy for short paper-trading experiments. This strategy deliberately seeks liquid 5-minute breakouts, then uses the 1-hour trend as a brake. It is a research starting point, not a promise of profit. """ from __future__ import annotations from datetime import datetime from typing import Optional import talib.abstract as ta from freqtrade.persistence import Trade from freqtrade.strategy import IStrategy, informative from pandas import DataFrame class Github_ammaryaser99_crypto_trading_bot__AggressiveMomentumScalper__20260625_003914(IStrategy): """Trade active 5m momentum and pullbacks when the higher timeframe is acceptable.""" INTERFACE_VERSION = 3 # The 5-minute chart finds entries; the decorator below supplies 1-hour trend data. timeframe = "5m" can_short = False process_only_new_candles = True startup_candle_count = 240 # A hard circuit breaker. This is intentionally aggressive for paper testing. stoploss = -0.065 trailing_stop = True trailing_stop_positive = 0.009 trailing_stop_positive_offset = 0.018 trailing_only_offset_is_reached = True # Fallback ROI schedule. custom_roi below makes the same intent explicit in code. minimal_roi = { "0": 0.018, "20": 0.012, "60": 0.006, "120": 0.0, } use_custom_roi = True use_exit_signal = True @property def protections(self) -> list[dict]: """Session circuit breakers required by Freqtrade 2026.5 and newer. Keep these in the strategy (rather than config.json) so strategy behavior, risk limits, and backtests remain coupled. """ return [ # Prevent immediate revenge re-entry into a just-closed pair. {"method": "CooldownPeriod", "stop_duration_candles": 2}, # Three stoploss-type exits in three hours lock all entries for two hours. { "method": "StoplossGuard", "lookback_period_candles": 36, "trade_limit": 3, "stop_duration_candles": 24, "only_per_pair": False, }, # A 15% drawdown across the prior 24 hours pauses new entries for four hours. { "method": "MaxDrawdown", "lookback_period_candles": 288, "trade_limit": 10, "stop_duration_candles": 48, "max_allowed_drawdown": 0.15, }, ] # Limit orders reduce taker fees/slippage in a scalping strategy. Emergency exits are # configured as market orders in config.json so a genuine stop is not left hanging. order_types = { "entry": "limit", "exit": "limit", "emergency_exit": "market", "force_exit": "market", "force_entry": "market", "stoploss": "market", "stoploss_on_exchange": False, } @informative("1h") def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """Higher-timeframe indicators prevent buying a 5m bounce in a weak macro trend.""" dataframe["ema_fast"] = ta.EMA(dataframe, timeperiod=20) dataframe["ema_slow"] = ta.EMA(dataframe, timeperiod=50) dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """Calculate fast momentum, volatility, and liquidity measurements.""" dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) dataframe["ema_fast"] = ta.EMA(dataframe, timeperiod=9) dataframe["ema_slow"] = ta.EMA(dataframe, timeperiod=21) macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9) dataframe["macd"] = macd["macd"] dataframe["macdsignal"] = macd["macdsignal"] dataframe["macdhist"] = macd["macdhist"] bands = ta.BBANDS( dataframe, timeperiod=20, nbdevup=2.0, nbdevdn=2.0, matype=0, ) dataframe["bb_upper"] = bands["upperband"] dataframe["bb_middle"] = bands["middleband"] dataframe["bb_lower"] = bands["lowerband"] dataframe["bb_width"] = (dataframe["bb_upper"] - dataframe["bb_lower"]) / dataframe[ "bb_middle" ] dataframe["atr"] = ta.ATR(dataframe, timeperiod=14) dataframe["atr_pct"] = dataframe["atr"] / dataframe["close"] dataframe["volume_mean_20"] = dataframe["volume"].rolling(20).mean() return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """Enter active liquid momentum and trend pullbacks. This version is intentionally more active for a short paper experiment: it accepts a softer 1h trend and allows pullback entries, while still requiring non-dead volume and ATR so it does not buy totally flat chop. """ trend_confirmed = ( (dataframe["ema_fast"] > dataframe["ema_slow"]) & (dataframe["close"] > dataframe["ema_slow"]) & ( (dataframe["ema_fast_1h"] > dataframe["ema_slow_1h"]) | ( (dataframe["close"] > dataframe["ema_fast_1h"]) & (dataframe["rsi_1h"] > 45) ) ) ) breakout_confirmed = ( (dataframe["rsi"] > 50) & (dataframe["rsi"] < 82) & (dataframe["macd"] > dataframe["macdsignal"]) & (dataframe["macdhist"] > 0) & (dataframe["close"] > dataframe["bb_middle"]) & (dataframe["bb_width"] > 0.008) ) pullback_confirmed = ( (dataframe["rsi"] > 42) & (dataframe["rsi"] < 68) & (dataframe["close"] > dataframe["ema_slow"]) & (dataframe["close"] <= dataframe["ema_fast"] * 1.01) & (dataframe["macdhist"] > dataframe["macdhist"].shift(1)) & (dataframe["bb_width"] > 0.006) ) liquid_and_tradeable = ( (dataframe["volume"] > dataframe["volume_mean_20"] * 0.85) # Avoid dead markets and violent one-candle chaos. 0.15%-7% ATR is # deliberately active, but still avoids completely flat candles. & (dataframe["atr_pct"] > 0.0015) & (dataframe["atr_pct"] < 0.07) & (dataframe["volume"] > 0) ) # A close above the upper band is allowed only with exceptional volume. continuation_is_supported = (dataframe["close"] <= dataframe["bb_upper"] * 1.006) | ( dataframe["volume"] > dataframe["volume_mean_20"] * 1.30 ) dataframe.loc[ trend_confirmed & breakout_confirmed & liquid_and_tradeable & continuation_is_supported, ["enter_long", "enter_tag"], ] = (1, "momentum_breakout_active") dataframe.loc[ trend_confirmed & pullback_confirmed & liquid_and_tradeable, ["enter_long", "enter_tag"], ] = (1, "trend_pullback_active") return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """Exit fading momentum quickly instead of waiting passively for the stoploss.""" macd_cross_down = (dataframe["macd"] < dataframe["macdsignal"]) & ( dataframe["macd"].shift(1) >= dataframe["macdsignal"].shift(1) ) momentum_failed = ( (dataframe["rsi"] < 43) | (macd_cross_down & (dataframe["close"] < dataframe["ema_fast"])) | ( (dataframe["close"] < dataframe["bb_middle"] * 0.997) & (dataframe["volume"] < dataframe["volume_mean_20"]) ) ) dataframe.loc[momentum_failed, "exit_long"] = 1 return dataframe def custom_roi( self, pair: str, trade: Trade, current_time: datetime, trade_duration: int, entry_tag: Optional[str], side: str, **kwargs: object, ) -> Optional[float]: """Demand a quick win early, then release capital if momentum takes too long.""" if entry_tag == "trend_pullback_active": if trade_duration < 20: return 0.014 if trade_duration < 60: return 0.009 if trade_duration < 120: return 0.004 return 0.0 if trade_duration < 30: return 0.018 if trade_duration < 60: return 0.012 if trade_duration < 120: return 0.006 return 0.0