Timeframe
1h
Direction
Long Only
Stoploss
-99.0%
Trailing Stop
No
ROI
0m: 10000.0%
Interface Version
3
Startup Candles
N/A
Indicators
4
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
"""
MTFTrendStack — multi-timeframe trend-following with 1d regime + 4h trend + 1h entry
Paradigm: trend-following
Hypothesis: v0.2.0's TrendEMAStack capped at Sharpe 0.36 on 1h-only because trend
signals on 1h crypto see too much noise. Stacking a 1d EMA200 regime
filter (only trade when above 200d) plus a 4h EMA9>EMA21 trend
confirmation should let the 1h entry trigger fire only inside genuine
multi-timeframe uptrends, lifting Sharpe and tightening DD.
Parent: root (paradigm-inspired by v0.2.0's TrendEMAStack but structurally different)
Created: pending — fill in after first commit
Status: active
Uses MTF: yes
"""
from pandas import DataFrame
import talib.abstract as ta
from freqtrade.strategy import IStrategy, informative
class MTFTrendStack(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
# 1d EMA200 needs 200 daily bars warmup ≈ 4800 hourly bars
startup_candle_count: int = 250
@informative("4h")
def populate_indicators_4h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Slower 4h EMA pair (13/34 vs prior 9/21) — clearer trend signal at 4h
dataframe["ema_fast"] = ta.EMA(dataframe, timeperiod=13)
dataframe["ema_slow"] = ta.EMA(dataframe, timeperiod=34)
dataframe["atr"] = ta.ATR(dataframe, timeperiod=14)
dataframe["atr_ma20"] = dataframe["atr"].rolling(20).mean()
return dataframe
@informative("1d")
def populate_indicators_1d(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe["ema200"] = ta.EMA(dataframe, timeperiod=200)
return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe["ema9"] = ta.EMA(dataframe, timeperiod=9)
dataframe["ema21"] = ta.EMA(dataframe, timeperiod=21)
dataframe["sma50"] = ta.SMA(dataframe, timeperiod=50)
dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(dataframe["close"] > dataframe["ema200_1d"]) # 1d bull regime
& (dataframe["ema_fast_4h"] > dataframe["ema_slow_4h"]) # 4h trend up (13/34)
& (dataframe["atr_4h"] > dataframe["atr_ma20_4h"]) # 4h ATR expansion (conviction)
& (dataframe["close"] > dataframe["ema9"]) # 1h pullback closed back above
& (dataframe["close"].shift(1) < dataframe["ema9"].shift(1)), # event-only (dropped redundant ema9>ema21 state)
"enter_long",
] = 1
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(dataframe["ema9"] < dataframe["ema21"]) # 1h trend break (responsive exit — patient exit hurts trend per r36)
| (dataframe["close"] < dataframe["ema200_1d"]), # regime break
"exit_long",
] = 1
return dataframe