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
"""
MeanRevBB — Bollinger lower-band bounce
Paradigm: mean-reversion
Hypothesis: BTC/ETH 1h bars that close below the lower Bollinger Band
(20-period, sigma=2.0) tend to mean-revert back to the middle band
within ~10-20 bars. No exit_profit_only trick — we want to measure
the raw BB bounce edge cleanly.
Parent: root
Created: pending-first-commit
Status: active
"""
from pandas import DataFrame
import talib.abstract as ta
from freqtrade.strategy import IStrategy
class MeanRevBB(IStrategy):
INTERFACE_VERSION = 3
timeframe = "1h"
can_short = False
minimal_roi = {"0": 100}
# No stop. Confirmed across multiple rounds: any stop tight enough to
# reduce DD (-5%, -15%) realizes recoverable losses and actually makes
# DD worse or leaves it flat while cutting profit.
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 = 210
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# BB period 15, sigma 2.0. Brackets confirmed: period 20/15/10 →
# 15 wins; sigma 2.5/2.0/1.8 → 2.0 wins.
bb = ta.BBANDS(dataframe, timeperiod=15, nbdevup=2.0, nbdevdn=2.0)
dataframe["bb_lower"] = bb["lowerband"]
dataframe["bb_middle"] = bb["middleband"]
dataframe["bb_upper"] = bb["upperband"]
# Regime EMA 100. Brackets: 200 (0.51), 100 (0.52), 75 (0.46),
# 50 (0.39). 100 is optimum.
dataframe["ema200"] = ta.EMA(dataframe, timeperiod=100)
dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
dataframe["vol_sma20"] = dataframe["volume"].rolling(20).mean()
dataframe["atr"] = ta.ATR(dataframe, timeperiod=14)
dataframe["atr_sma20"] = dataframe["atr"].rolling(20).mean()
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# 1-bar confirmed reversal. Magnitude gate (round 67, prev < bb_lower -
# 0.5*ATR) killed MR — shallow BB touches ARE the edge in bull regime;
# deeper oversold = capitulation = worse bounces.
prev_below_lower = dataframe["close"].shift(1) < dataframe["bb_lower"].shift(1)
now_above_lower = dataframe["close"] > dataframe["bb_lower"]
bull_regime = dataframe["close"] > dataframe["ema200"]
# Volume bracket: 1.0x (0.52), 1.25x (0.32), 1.5x (0.25). 1.0x wins.
vol_expansion = dataframe["volume"] > dataframe["vol_sma20"]
# RSI<75 (round 76) was a no-op here — MR entries are inherently
# low-RSI (price just dropped below lower BB).
dataframe.loc[
prev_below_lower & now_above_lower & bull_regime & vol_expansion,
"enter_long",
] = 1
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Exit at upper band + RSI>75. Bracket 65/70/75/80 → 75 optimum.
# Patient exit lets winners extend; 80 too strict (trades 139→84).
dataframe.loc[
(dataframe["close"] >= dataframe["bb_upper"])
& (dataframe["rsi"] > 75),
"exit_long",
] = 1
return dataframe