# source: https://raw.githubusercontent.com/aragier/tradingStrategies/ba3f2fd3be7a23d51a2f518cb043f0db05091e41/user_data/strategies/BtcEma9_21Scalp_v2.py # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # flake8: noqa: F401 # isort: skip_file # --- Do not remove these imports --- import numpy as np import pandas as pd from datetime import datetime, timedelta, timezone from pandas import DataFrame from typing import Optional, Union from freqtrade.strategy import ( IStrategy, Trade, Order, PairLocks, informative, BooleanParameter, CategoricalParameter, DecimalParameter, IntParameter, RealParameter, timeframe_to_minutes, timeframe_to_next_date, timeframe_to_prev_date, merge_informative_pair, stoploss_from_absolute, stoploss_from_open, ) # -------------------------------- import talib.abstract as ta from technical import qtpylib class Github_aragier_tradingStrategies__BtcEma9_21Scalp_v2__20260327_161149(IStrategy): """ EMA 9/21 Crossover Scalping Strategy v2 — with multi-timeframe filter, MACD-confirmed exits, and volume/RSI improvements. Improvements over v1: - 1h EMA50 trend direction filter (only trade with the trend) - Volume > 1.5x SMA(20) filter on entry - Tighter RSI bands: 50-70 for long, 30-50 for short - Exit signal requires MACD histogram confirmation - RSI extreme exits (>75 long, <25 short) Designed for 5m timeframe, BTC futures (long + short). """ INTERFACE_VERSION = 3 can_short: bool = True # --- Minimal ROI --- minimal_roi = { "30": 0.01, "15": 0.02, "0": 0.03, } # --- Stoploss --- stoploss = -0.02 # --- Trailing Stop --- trailing_stop = True trailing_stop_positive = 0.005 trailing_stop_positive_offset = 0.01 trailing_only_offset_is_reached = True # --- Timeframe --- timeframe = "5m" # --- General settings --- process_only_new_candles = True use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False # --- Hyperoptable Parameters --- # EMA periods ema_fast_period = IntParameter(5, 15, default=9, space="buy", optimize=True, load=True) ema_slow_period = IntParameter(15, 30, default=21, space="buy", optimize=True, load=True) # RSI entry thresholds rsi_long_min = IntParameter(45, 55, default=50, space="buy", optimize=True, load=True) rsi_long_max = IntParameter(65, 80, default=70, space="buy", optimize=True, load=True) rsi_short_min = IntParameter(20, 35, default=30, space="sell", optimize=True, load=True) rsi_short_max = IntParameter(45, 55, default=50, space="sell", optimize=True, load=True) # RSI exit extremes rsi_exit_long = IntParameter(70, 85, default=75, space="sell", optimize=True, load=True) rsi_exit_short = IntParameter(15, 30, default=25, space="sell", optimize=True, load=True) # ADX threshold adx_threshold = IntParameter(20, 35, default=25, space="buy", optimize=True, load=True) # Volume multiplier volume_factor = DecimalParameter(1.0, 2.5, default=1.5, decimals=1, space="buy", optimize=True, load=True) # 1h EMA trend lookback (how many 1h candles back to compare for trend direction) ema_1h_lookback = IntParameter(2, 6, default=3, space="buy", optimize=True, load=True) # Startup candles: 1h EMA50 needs 50 candles on 1h = 50*12 = 600 on 5m, plus buffer startup_candle_count: int = 650 # --- Order types --- order_types = { "entry": "limit", "exit": "limit", "stoploss": "market", "stoploss_on_exchange": False, } order_time_in_force = {"entry": "GTC", "exit": "GTC"} # --- Plot config for FreqUI --- plot_config = { "main_plot": { "ema_fast": {"color": "blue"}, "ema_slow": {"color": "orange"}, }, "subplots": { "RSI": { "rsi": {"color": "red"}, }, "ADX": { "adx": {"color": "purple"}, }, "MACD": { "macdhist": {"color": "green", "type": "bar"}, }, }, } # --- 1h Informative Timeframe --- @informative("1h") def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe["ema_50"] = ta.EMA(dataframe, timeperiod=50) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # EMA - Fast and Slow dataframe["ema_fast"] = ta.EMA(dataframe, timeperiod=self.ema_fast_period.value) dataframe["ema_slow"] = ta.EMA(dataframe, timeperiod=self.ema_slow_period.value) # RSI dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) # ADX - trend strength dataframe["adx"] = ta.ADX(dataframe, timeperiod=14) # MACD - for exit confirmation macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9) dataframe["macd"] = macd["macd"] dataframe["macdsignal"] = macd["macdsignal"] dataframe["macdhist"] = macd["macdhist"] # Volume SMA for volume filter dataframe["volume_sma"] = ta.SMA(dataframe["volume"], timeperiod=20) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # --- Long Entry --- # EMA9 crosses above EMA21 # + 1h EMA50 rising (bullish higher-timeframe trend) # + RSI in 50-70 range (momentum up, not exhausted) # + ADX > threshold (trending) # + Volume spike (conviction) dataframe.loc[ ( (qtpylib.crossed_above(dataframe["ema_fast"], dataframe["ema_slow"])) & (dataframe["ema_50_1h"] > dataframe["ema_50_1h"].shift(self.ema_1h_lookback.value)) & (dataframe["rsi"] > self.rsi_long_min.value) & (dataframe["rsi"] < self.rsi_long_max.value) & (dataframe["adx"] > self.adx_threshold.value) & (dataframe["volume"] > (dataframe["volume_sma"] * self.volume_factor.value)) ), "enter_long", ] = 1 # --- Short Entry --- # EMA9 crosses below EMA21 # + 1h EMA50 falling (bearish higher-timeframe trend) # + RSI in 30-50 range (momentum down, not oversold) # + ADX > threshold (trending) # + Volume spike (conviction) dataframe.loc[ ( (qtpylib.crossed_below(dataframe["ema_fast"], dataframe["ema_slow"])) & (dataframe["ema_50_1h"] < dataframe["ema_50_1h"].shift(self.ema_1h_lookback.value)) & (dataframe["rsi"] > self.rsi_short_min.value) & (dataframe["rsi"] < self.rsi_short_max.value) & (dataframe["adx"] > self.adx_threshold.value) & (dataframe["volume"] > (dataframe["volume_sma"] * self.volume_factor.value)) ), "enter_short", ] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # --- Exit Long --- # Option A: Confirmed reversal — EMA crosses back AND MACD histogram negative # Option B: RSI extreme — momentum exhaustion take-profit dataframe.loc[ ( ( (qtpylib.crossed_below(dataframe["ema_fast"], dataframe["ema_slow"])) & (dataframe["macdhist"] < 0) ) | (dataframe["rsi"] > self.rsi_exit_long.value) ) & (dataframe["volume"] > 0), "exit_long", ] = 1 # --- Exit Short --- # Option A: Confirmed reversal — EMA crosses back AND MACD histogram positive # Option B: RSI extreme — momentum exhaustion take-profit dataframe.loc[ ( ( (qtpylib.crossed_above(dataframe["ema_fast"], dataframe["ema_slow"])) & (dataframe["macdhist"] > 0) ) | (dataframe["rsi"] < self.rsi_exit_short.value) ) & (dataframe["volume"] > 0), "exit_short", ] = 1 return dataframe