Forex Trend Strategy with RSI and EMAs

Code Freqtrade

Summary

This strategy is designed to capture short-term forex trends on a four-hour timeframe. It enters long when the 10-period RSI, calculated from the midpoint of open and close, crosses above 50 at the same time that the five-period EMA crosses above the ten-period EMA. The ADX must be above 25, and the candle must have nonzero volume.

The exit rule looks for the corresponding downward RSI and EMA crossovers while ADX remains above 25 and volume is nonzero. The configuration also specifies a stop loss, a trailing stop, and staged return targets, though these settings are framework parameters rather than evidence of performance. The document provides implementation logic but no backtest, market selection, short-entry rules, transaction-cost analysis, or risk-adjusted results, so its effectiveness cannot be assessed from the strategy description alone.

Key ideas

The strategy seeks short-term forex trends using four-hour candles.
A long entry requires RSI and a fast EMA to cross above their respective thresholds together.
ADX above 25 acts as a trend-strength filter, and zero-volume candles are excluded.
The exit signal uses downward RSI and EMA crossovers with the same ADX and volume conditions.
The document gives configuration settings but provides no performance evidence or cost analysis.

Tags

Forex Trend following Momentum Technical indicators Risk management

Full text

# hlhb.py


```py
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement

import numpy as np  # noqa
import pandas as pd  # noqa
from pandas import DataFrame
from freqtrade.strategy import IStrategy
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib


class hlhb(IStrategy):
    """
    The HLHB ("Huck loves her bucks!") System simply aims to catch short-term forex trends.
    More information in https://www.babypips.com/trading/forex-hlhb-system-explained
    """

    INTERFACE_VERSION: int = 3

    position_stacking = "True"

    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi".
    minimal_roi = {
        "0": 0.6225,
        "703": 0.2187,
        "2849": 0.0363,
        "5520": 0
    }

    # Optimal stoploss designed for the strategy.
    # This attribute will be overridden if the config file contains "stoploss".
    stoploss = -0.3211

    # Trailing stoploss
    trailing_stop = True
    trailing_stop_positive = 0.0117
    trailing_stop_positive_offset = 0.0186
    trailing_only_offset_is_reached = True

    # Optimal timeframe for the strategy.
    timeframe = '4h'

    # Run "populate_indicators()" only for new candle.
    process_only_new_candles = True

    # These values can be overridden in the "ask_strategy" section in the config.
    use_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = True

    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 30

    # Optional order type mapping.
    order_types = {
        'entry': 'limit',
        'exit': 'limit',
        'stoploss': 'market',
        'stoploss_on_exchange': False
    }

    # Optional order time in force.
    order_time_in_force = {
        'entry': 'gtc',
        'exit': 'gtc'
    }

    plot_config = {
        # Main plot indicators (Moving averages, ...)
        'main_plot': {
            'ema5': {},
            'ema10': {},
        },
        'subplots': {
            # Subplots - each dict defines one additional plot
            "RSI": {
                'rsi': {'color': 'red'},
            },
            "ADX": {
                'adx': {},
            }
        }
    }

    def informative_pairs(self):
        return []

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['hl2'] = (dataframe["close"] + dataframe["open"]) / 2

        # Momentum Indicators
        # ------------------------------------

        # RSI
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=10, price='hl2')

        # # EMA - Exponential Moving Average
        dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
        dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)

        # ADX
        dataframe['adx'] = ta.ADX(dataframe)

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (qtpylib.crossed_above(dataframe['rsi'], 50)) &
                (qtpylib.crossed_above(dataframe['ema5'], dataframe['ema10'])) &
                (dataframe['adx'] > 25) &
                (dataframe['volume'] > 0)  # Make sure Volume is not 0
            ),
            'enter_long'] = 1

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (qtpylib.crossed_below(dataframe['rsi'], 50)) &
                (qtpylib.crossed_below(dataframe['ema5'], dataframe['ema10'])) &
                (dataframe['adx'] > 25) &
                (dataframe['volume'] > 0)  # Make sure Volume is not 0
            ),
            'exit_long'] = 1
        return dataframe


```

Shown in full with attribution under the source's licence. Licence: GPL-3.0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.

A Four-Hour Forex Trend Strategy Using RSI, EMA Crossovers, and ADX

Summary

Key ideas

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