Timeframe
1h
Direction
Long Only
Stoploss
-99.0%
Trailing Stop
No
ROI
0m: 10000.0%
Interface Version
3
Startup Candles
N/A
Indicators
4
"""
BTCLeaderBreakV4 — BTC 4h Donchian-15 break triggers entries on all pairs
Paradigm: breakout (cross-pair leader→follower)
Hypothesis: v0.3.0's BTCLeaderBreakX hit 1.07 Sharpe on bull-only 2023-2025
data using a BTC 4h Donchian-10 break as the trigger for trades
on every pair, with local volume confirmation and SMA50 patient
exit (Findings 1+2). v0.4.0's regime-mixed timerange should
stress-test that paradigm — does the cross-pair leader signal
survive 2022 winter where BTC's own breakouts went very wrong
briefly? Direct test, not a copy: Donchian-15 (less aggressive
than v0.3.0's terminal 10), 4h regime gate (BTC 4h EMA50 > 4h
EMA200), local-pair volume confirmation, SMA50 exit, and
vol-target sizing. If V4 reaches ≥0.9 Sharpe in regime mix,
v0.3.0's leader paradigm is regime-robust. If it collapses
(e.g. <0.5), the v0.3.0 result was a bull-regime artifact.
Replaces ChannelADXTrend (killed r8 — trend paradigm capped at
+0.05 on this universe; freeing slot for the more interesting
cross-regime test).
Parent: root (paradigm-resurrection of v0.3.0 BTCLeaderBreakX with v0.4.0
affordances; structurally distinct from VolBreakoutSized which
uses per-pair Donchian-24, no cross-pair leader)
Created: pending — fill in after first commit
Status: active
Uses MTF: yes (BTC 4h informative + 4h vol-target + 1h trade base)
"""
from pandas import DataFrame
import talib.abstract as ta
from freqtrade.strategy import IStrategy, informative
class BTCLeaderBreakV4(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", "BTC/USDT")
def populate_indicators_btc_4h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# r11: Donchian-10 (revert from 8). r10 confirmed 10 IS the local
# optimum across both bull-only (v0.3.0) AND regime-mixed (v0.4.0)
# data. Cross-regime parameter robustness on this knob.
dataframe["donchian_high_10"] = dataframe["high"].rolling(10).max().shift(1)
dataframe["ema50"] = ta.EMA(dataframe, timeperiod=50)
dataframe["ema200"] = ta.EMA(dataframe, timeperiod=200)
return dataframe
@informative("4h")
def populate_indicators_4h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Local-pair 4h ATR for vol-target sizing.
dataframe["atr"] = ta.ATR(dataframe, timeperiod=14)
dataframe["atr_pct"] = dataframe["atr"] / dataframe["close"]
return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# r14: SMA75→SMA100. r13 SMA50→75 was a +0.10 Sharpe win on V4.
# Continue patient-exit trend further to find the local optimum.
dataframe["sma100"] = ta.SMA(dataframe, timeperiod=100)
dataframe["volume_sma20"] = dataframe["volume"].rolling(20).mean()
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# BTC 4h Donchian-15 break (leader signal — same on every pair),
# AND BTC 4h EMA50>EMA200 (BTC structural bull, slow regime),
# AND local-pair 1h volume confirmation (v0.3.0 Finding 1: local
# volume on trade pair >> signal-source volume).
dataframe.loc[
(dataframe["btc_usdt_close_4h"] > dataframe["btc_usdt_donchian_high_10_4h"])
& (dataframe["btc_usdt_ema50_4h"] > dataframe["btc_usdt_ema200_4h"])
& (dataframe["volume"] > 1.3 * dataframe["volume_sma20"]),
"enter_long",
] = 1
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# SMA50 patient ride-the-move exit (v0.3.0 Finding 2 — breakouts
# benefit from slow exits, not responsive ones).
dataframe.loc[dataframe["close"] < dataframe["sma100"], "exit_long"] = 1
return dataframe
def custom_stake_amount(
self,
pair: str,
current_time,
current_rate: float,
proposed_stake: float,
min_stake,
max_stake: float,
leverage: float,
entry_tag: str,
side: str,
**kwargs,
) -> float:
# Vol-target on the trade pair's 4h ATR (NOT BTC's). Each pair
# de-risks based on its own volatility — BTC and SOL get different
# stakes for the same BTC-leader signal.
df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
if df.empty or "atr_pct_4h" not in df.columns:
return proposed_stake
atr_pct = df["atr_pct_4h"].iloc[-1]
if atr_pct != atr_pct or atr_pct <= 0:
return proposed_stake
# r20 (final): vol_target 0.010→0.005 (mirror Vol r19).
vol_target = 0.005
scale = min(1.0, vol_target / atr_pct)
stake = proposed_stake * scale
return max(min_stake or 0.0, min(max_stake, stake))