Timeframe
1h
Direction
Long Only
Stoploss
-99.0%
Trailing Stop
No
ROI
0m: 10000.0%
Interface Version
3
Startup Candles
N/A
Indicators
5
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
"""
MomentumMTFConfluence — multi-TF MACD momentum stack on regime-gated 1d uptrend
Paradigm: momentum
Hypothesis: v0.3.0's MACDMomentumMTF capped at 0.41 Sharpe on bull-only
data — diagnosis was "v0.2.0's 0.67 was BTC/ETH-tuning that
doesn't generalize". v0.4.0 question: does momentum survive
regime mix at all, or does it collapse in 2022 winter where
sustained uptrends are rare? Three-TF stack: 1d regime gate
(close > 1d SMA50) defines tradeable universe; 4h MACD > MACD
signal sets directional pulse; 1h close cross-up of 1h EMA20
times the entry. Exit on 4h MACD < signal (regime-pulse break).
Equal-weight (no sizing) — keeps this distinct from the two
sized-breakout strategies. If MomentumMTFConfluence reaches
≥0.5 in regime mix, momentum paradigm is regime-survivable.
If <0.2 it confirms v0.3.0's "momentum has BTC/ETH-bull
ceiling" diagnosis transfers to mixed regimes too.
Parent: root (paradigm-resurrection of v0.3.0 MACDMomentumMTF, structurally
leaner — single MACD trigger per TF, no MACD>0/RSI/ATR/strength stack)
Created: pending — fill in after first commit
Status: active
Uses MTF: yes (1d regime + 4h MACD pulse + 1h entry)
"""
from pandas import DataFrame
import talib.abstract as ta
from freqtrade.strategy import IStrategy, informative
class MomentumMTFConfluence(IStrategy):
INTERFACE_VERSION = 3
timeframe = "1h"
can_short = False
minimal_roi = {"0": 100}
stoploss = -0.99
trailing_stop = False
process_only_new_candles = True
use_exit_signal = True
exit_profit_only = False
ignore_roi_if_entry_signal = False
startup_candle_count: int = 250
@informative("4h")
def populate_indicators_4h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9)
dataframe["macd"] = macd["macd"]
dataframe["macdsignal"] = macd["macdsignal"]
return dataframe
@informative("1d")
def populate_indicators_1d(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe["sma50"] = ta.SMA(dataframe, timeperiod=50)
return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe["ema20"] = ta.EMA(dataframe, timeperiod=20)
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# 1d regime + 4h MACD bullish + 1h fresh close-cross-up of EMA20.
dataframe.loc[
(dataframe["close"] > dataframe["sma50_1d"])
& (dataframe["macd_4h"] > dataframe["macdsignal_4h"])
& (dataframe["close"] > dataframe["ema20"])
& (dataframe["close"].shift(1) <= dataframe["ema20"].shift(1)),
"enter_long",
] = 1
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Exit on 4h momentum break (MACD crosses below signal) — responsive
# exit per v0.3.0 Finding 2 ("trend/momentum paradigms benefit from
# responsive exits, not patient SMA-style").
dataframe.loc[
dataframe["macd_4h"] < dataframe["macdsignal_4h"],
"exit_long",
] = 1
return dataframe