EMA Crossover Entries with Heikin-Ashi Confirmation
Summary
This five-minute long-only strategy combines exponential moving average crossovers with Heikin-Ashi candle direction. It enters when the 20-period EMA crosses above the 50-period EMA, the Heikin-Ashi close is above the 20-period EMA, and the Heikin-Ashi candle is bullish. It exits when the 50-period EMA crosses above the 100-period EMA while the Heikin-Ashi close is below the 20-period EMA and the candle is bearish.
The strategy also specifies a stepped minimal return schedule, a 10% stop loss, limit entry and exit orders, and no trailing stop. These are configuration settings rather than evidence of performance: the document provides no backtest results, market, or validation method. The rules may produce delayed signals and are sensitive to timeframe and market conditions. The stated return and stop settings can also be overridden by configuration, so actual behavior depends on the running setup.
Key ideas
- The strategy calculates 20, 50, and 100-period exponential moving averages and Heikin-Ashi candles.
- Long entries require a 20-period EMA crossover above the 50-period EMA plus bullish Heikin-Ashi confirmation.
- Long exits require the 50-period EMA to cross above the 100-period EMA alongside bearish candle and price conditions.
- The configuration specifies a five-minute timeframe, a 10% stop loss, and no trailing stop.
- The document gives no performance results or evidence that the rules are profitable.
Tags
Full text
# Strategy001.py
```py
# --- Do not remove these libs ---
from freqtrade.strategy import IStrategy
from typing import Dict, List
from functools import reduce
from pandas import DataFrame
# --------------------------------
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
class Strategy001(IStrategy):
"""
Strategy 001
author@: Gerald Lonlas
github@: https://github.com/freqtrade/freqtrade-strategies
How to use it?
> python3 ./freqtrade/main.py -s Strategy001
"""
INTERFACE_VERSION: int = 3
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi"
minimal_roi = {
"60": 0.01,
"30": 0.03,
"20": 0.04,
"0": 0.05
}
# Optimal stoploss designed for the strategy
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.10
# Optimal timeframe for the strategy
timeframe = '5m'
# trailing stoploss
trailing_stop = False
trailing_stop_positive = 0.01
trailing_stop_positive_offset = 0.02
# run "populate_indicators" only for new candle
process_only_new_candles = True
# Experimental settings (configuration will overide these if set)
use_exit_signal = True
exit_profit_only = True
ignore_roi_if_entry_signal = False
# Optional order type mapping
order_types = {
'entry': 'limit',
'exit': 'limit',
'stoploss': 'market',
'stoploss_on_exchange': False
}
def informative_pairs(self):
"""
Define additional, informative pair/interval combinations to be cached from the exchange.
These pair/interval combinations are non-tradeable, unless they are part
of the whitelist as well.
For more information, please consult the documentation
:return: List of tuples in the format (pair, interval)
Sample: return [("ETH/USDT", "5m"),
("BTC/USDT", "15m"),
]
"""
return []
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
Performance Note: For the best performance be frugal on the number of indicators
you are using. Let uncomment only the indicator you are using in your strategies
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
"""
dataframe['ema20'] = ta.EMA(dataframe, timeperiod=20)
dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
heikinashi = qtpylib.heikinashi(dataframe)
dataframe['ha_open'] = heikinashi['open']
dataframe['ha_close'] = heikinashi['close']
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
:return: DataFrame with buy column
"""
dataframe.loc[
(
qtpylib.crossed_above(dataframe['ema20'], dataframe['ema50']) &
(dataframe['ha_close'] > dataframe['ema20']) &
(dataframe['ha_open'] < dataframe['ha_close']) # green bar
),
'enter_long'] = 1
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
:return: DataFrame with buy column
"""
dataframe.loc[
(
qtpylib.crossed_above(dataframe['ema50'], dataframe['ema100']) &
(dataframe['ha_close'] < dataframe['ema20']) &
(dataframe['ha_open'] > dataframe['ha_close']) # red bar
),
'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.