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添加新的策略

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jackyu66git
2026-02-04 22:53:52 +08:00
parent 98e309802a
commit 39b5103858
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strategies/EMA_Pattern.py
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# --- Do not remove these libs ---
from statistics import median
from freqtrade.strategy import IStrategy
from technical.util import resample_to_interval, resampled_merge
from pandas import DataFrame
import talib.abstract as ta
from technical import qtpylib
import numpy as np
# 1小时短线趋势追踪策略 V7 - EMA + MACD 综合趋势判断
# 使用 EMA8, EMA24, EMA72, EMA168 + MACD 识别趋势阶段和强度
# freqtrade backtesting -c ./user_data/Chan/config/EMA_Pattern.json --strategy EMA_Pattern --strategy-path ./user_data/Chan/strategies --timerange=20250101-
# freqtrade download-data -c ./user_data/Chan/config/EMA_Pattern.json -t 1h --pairs BTC/USDT:USDT --timerange=20250101-
### Now you can use logger.info('asfd') to log
# freqtrade plot-dataframe --strategy EMA_Pattern --datadir user_data/data/binance -c ./user_data/Chan/EMA_Pattern.json --timerange=20250309-
# freqtrade backtesting -c ./user_data/Chan/config/EMA_Pattern.json --strategy EMA_Pattern --strategy-path ./user_data/Chan/strategies --timerange=20251030-
# freqtrade download-data -c ./user_data/Chan/config/EMA_Pattern.json -t 1m 3m 5m 15m 30m 1h --pairs BTC/USDT:USDT --timerange=20250405-
# freqtrade download-data -c ./user_data/Chan/config/EMA_Pattern.json -t 1m 1h 1d 1M --pairs BTC/USDT --timerange=20170101-
# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces roi --strategy EMA_Pattern --strategy-path ./user_data/Chan/strategies -c ./user_data/Chan/config/EMA_Pattern.json -e 200 --timerange=20250201-20250901
# freqtrade edge -c ./user_data/Chan/config/EMA_Pattern.json --strategy EMA_Pattern --strategy-path ./user_data/Chan/strategies --timerange 20250721-20250901
# freqtrade plot-dataframe -c ./user_data/Chan/config/EMA_Pattern.json --strategy EMA_Pattern --strategy-path ./user_data/Chan/strategies --timerange 20250721-20250901
class EMA_Pattern(IStrategy):
time1h = 1440
can_short: bool = True
timeframe: str = "1m"
process_only_new_candles: bool = False
"""
1小时K线短线趋势追踪策略 V7 - EMA + MACD
核心逻辑:
1. EMA多头排列 + MACD金叉确认 = 趋势启动（最佳入场）
2. EMA发散 + MACD动能增加 = 趋势发展（可追涨）
3. MACD动能减弱/顶背离 = 趋势成熟（减仓）
4. EMA死叉 + MACD死叉 = 趋势结束（出场）
"""
can_short: bool = True
timeframe: str = "1h"
process_only_new_candles: bool = True
# EMA参数
ema_fast: int = 8
ema_short: int = 24
ema_mid: int = 72
ema_long: int = 168
# 趋势强度阈值（放宽以增加交易机会）
min_trend_strength: int = 65
# ADX阈值
min_adx: int = 25 # 有趋势就交易
# ROI - V6最佳参数（无杠杆）
minimal_roi = {
"120": 0.02, # 5天后 2%止盈
"72": 0.03, # 3天后 3%止盈
"24": 0.05, # 1天后 5%止盈
"0": 0.08, # 立即 8%止盈
}
# 固定止损
stoploss: float = -0.05 # 5%止损
# 追踪止损
trailing_stop: bool = True
trailing_stop_positive: float = 0.025 # 盈利2.5%后启动
trailing_stop_positive_offset: float = 0.04 # 盈利4%后才触发
trailing_only_offset_is_reached: bool = True
# 禁用自定义止损（让追踪止损工作）
use_custom_stoploss: bool = False
# 只做多（做空效果差）
can_short: bool = False
startup_candle_count: int = 250
# ROI 与止损可根据需要在配置中覆盖
minimal_roi = {
"60": 0.005,
"30": 0.01,
"0": 0.02,
}
stoploss: float = -0.30
# 需要的历史K线数量（包含EMA等指标预热）
startup_candle_count: int = 200
def custom_stoploss(self, pair: str, trade, current_time, current_rate,
current_profit, after_fill, **kwargs) -> float:
"""
动态止损 V3 - 保护利润但不过早出场：
1. 盈利超过6%，止损移到盈利3%（锁定一半利润）
2. 盈利超过3%，止损移到盈利1%
3. 盈利超过1.5%，止损移到保本
4. 长时间亏损才考虑缩紧止损
"""
# 持仓时间（小时）
trade_duration = (current_time - trade.open_date_utc).total_seconds() / 3600
# 盈利时动态止损 - 阶梯式保护利润
if current_profit > 0.06:
# 盈利超过6%，锁定3%利润
return -0.03
elif current_profit > 0.03:
# 盈利超过3%，锁定1%利润
return -0.02
elif current_profit > 0.015:
# 盈利超过1.5%，移到保本
return -0.005
# 长时间持仓亏损（超过96小时=4天），才缩紧止损
if trade_duration > 96 and current_profit < -0.03:
return -0.04 # 缩紧到4%
# 默认使用配置的止损
return self.stoploss
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
if dataframe is None or dataframe.empty:
return dataframe
dataframe = self.add_indicators(dataframe)
return dataframe
def add_indicators(self, dataframe: DataFrame) -> DataFrame:
macd = ta.MACD(dataframe, timeperiod=12, fastperiod=12, slowperiod=26, signalperiod=9)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
dataframe['ema6'] = ta.EMA(dataframe, timeperiod=6)
dataframe['ema12'] = ta.EMA(dataframe, timeperiod=12)
dataframe['ema24'] = ta.EMA(dataframe, timeperiod=24)
dataframe['ema52'] = ta.EMA(dataframe, timeperiod=52)
dataframe['adx'] = ta.ADX(dataframe, timeperiod=14)
dataframe['strong_trend'] = dataframe['adx'] > 25
dataframe['UP_Pattern'] = (dataframe['ema6'] > dataframe['ema12']) & (dataframe['ema12'] > dataframe['ema24']) & (dataframe['ema24'] > dataframe['ema52'])
dataframe['DOWN_Pattern'] = (dataframe['ema6'] < dataframe['ema12']) & (dataframe['ema12'] < dataframe['ema24']) & (dataframe['ema24'] < dataframe['ema52'])
dataframe['UP_Confirm'] = (dataframe['ema6'] > dataframe['ema6'].shift(1)) & (dataframe['ema12'] > dataframe['ema12'].shift(1)) & (dataframe['ema24'] > dataframe['ema24'].shift(1)) & (dataframe['ema52'] > dataframe['ema52'].shift(1))
dataframe['DOWN_Confirm'] = (dataframe['ema6'] < dataframe['ema6'].shift(1)) & (dataframe['ema12'] < dataframe['ema12'].shift(1)) & (dataframe['ema24'] < dataframe['ema24'].shift(1)) & (dataframe['ema52'] < dataframe['ema52'].shift(1))
dataframe['EMA52_Cross_EMA24_UP'] = (dataframe['ema52'] < dataframe['ema24']) & (dataframe['ema52'].shift(1) > dataframe['ema24'].shift(1))
dataframe['EMA52_Cross_EMA24_DOWN'] = (dataframe['ema52'] > dataframe['ema24']) & (dataframe['ema52'].shift(1) < dataframe['ema24'].shift(1))
dataframe['Price_Above_EMA52'] = (dataframe['close'] > dataframe['ema52'])
dataframe['Price_Below_EMA52'] = (dataframe['close'] < dataframe['ema52'])
dataframe['MACD_Above_Zero'] = (dataframe['macd'] > 0) & (dataframe['macdsignal'] > 0)
dataframe['MACD_Below_Zero'] = (dataframe['macd'] < 0) & (dataframe['macdsignal'] < 0)
dataframe['BUY_END'] = dataframe['close'] < dataframe['ema52']
dataframe['SELL_END'] = dataframe['close'] > dataframe['ema52']
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
if dataframe is None or dataframe.empty:
return dataframe
dataframe.loc[
(
(dataframe['UP_Pattern']) &
(dataframe['UP_Confirm']) &
(dataframe['Price_Above_EMA52']) &
(dataframe['MACD_Above_Zero']) &
(dataframe['EMA52_Cross_EMA24_UP']) &
(dataframe['strong_trend'])
),
["enter_long", "enter_tag"],
] = (1, "ema_up_trend")
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
if dataframe is None or dataframe.empty:
return dataframe
# ==================== EMA指标 ====================
dataframe['ema8'] = ta.EMA(dataframe, timeperiod=self.ema_fast)
dataframe['ema24'] = ta.EMA(dataframe, timeperiod=self.ema_short)
dataframe['ema72'] = ta.EMA(dataframe, timeperiod=self.ema_mid)
dataframe['ema168'] = ta.EMA(dataframe, timeperiod=self.ema_long)
# ==================== MACD指标 ====================
macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9)
dataframe['macd'] = macd['macd']
dataframe['macd_signal'] = macd['macdsignal']
dataframe['macd_hist'] = macd['macdhist']
# MACD辅助指标
dataframe['macd_hist_change'] = dataframe['macd_hist'] - dataframe['macd_hist'].shift(1)
dataframe['macd_hist_ma'] = dataframe['macd_hist'].rolling(5).mean()
# ADX趋势强度
dataframe['adx'] = ta.ADX(dataframe, timeperiod=14)
# ATR波动率
dataframe['atr'] = ta.ATR(dataframe, timeperiod=14)
dataframe['atr_pct'] = dataframe['atr'] / dataframe['close'] * 100
# 成交量
dataframe['volume_ma'] = dataframe['volume'].rolling(20).mean()
dataframe['volume_ratio'] = dataframe['volume'] / dataframe['volume_ma']
# ==================== 均线排列 ====================
dataframe['bull_align'] = (
(dataframe['ema8'] > dataframe['ema24']) &
(dataframe['ema24'] > dataframe['ema72']) &
(dataframe['ema72'] > dataframe['ema168'])
)
dataframe['bear_align'] = (
(dataframe['ema8'] < dataframe['ema24']) &
(dataframe['ema24'] < dataframe['ema72']) &
(dataframe['ema72'] < dataframe['ema168'])
)
# 简化排列（短中期）
dataframe['bull_align_short'] = (
(dataframe['ema8'] > dataframe['ema24']) &
(dataframe['ema24'] > dataframe['ema72'])
)
dataframe['bear_align_short'] = (
(dataframe['ema8'] < dataframe['ema24']) &
(dataframe['ema24'] < dataframe['ema72'])
)
# ==================== 均线斜率 ====================
lookback = 5
dataframe['slope_ema8'] = (dataframe['ema8'] - dataframe['ema8'].shift(lookback)) / dataframe['ema8'].shift(lookback) * 100
dataframe['slope_ema24'] = (dataframe['ema24'] - dataframe['ema24'].shift(lookback)) / dataframe['ema24'].shift(lookback) * 100
dataframe['slope_ema72'] = (dataframe['ema72'] - dataframe['ema72'].shift(lookback)) / dataframe['ema72'].shift(lookback) * 100
dataframe['slope_ema168'] = (dataframe['ema168'] - dataframe['ema168'].shift(lookback)) / dataframe['ema168'].shift(lookback) * 100
dataframe['slope_bull_confirm'] = (
(dataframe['slope_ema8'] > 0) &
(dataframe['slope_ema24'] > 0) &
(dataframe['slope_ema72'] > 0)
)
dataframe['slope_bear_confirm'] = (
(dataframe['slope_ema8'] < 0) &
(dataframe['slope_ema24'] < 0) &
(dataframe['slope_ema72'] < 0)
)
# ==================== 均线间距 ====================
dataframe['spread_total'] = (dataframe['ema8'] - dataframe['ema168']) / dataframe['ema168'] * 100
dataframe['spread_short'] = (dataframe['ema8'] - dataframe['ema24']) / dataframe['ema24'] * 100
dataframe['spread_change'] = dataframe['spread_total'] - dataframe['spread_total'].shift(lookback)
dataframe['spread_ma'] = dataframe['spread_total'].rolling(20).mean()
spread_abs = dataframe['spread_total'].abs()
dataframe['spread_pct_30'] = spread_abs.rolling(50, min_periods=20).quantile(0.3)
dataframe['spread_pct_70'] = spread_abs.rolling(50, min_periods=20).quantile(0.7)
# ==================== MACD交叉信号 ====================
# MACD金叉（MACD线上穿信号线）
dataframe['macd_golden_cross'] = (
(dataframe['macd'] > dataframe['macd_signal']) &
(dataframe['macd'].shift(1) <= dataframe['macd_signal'].shift(1))
)
# MACD死叉
dataframe['macd_death_cross'] = (
(dataframe['macd'] < dataframe['macd_signal']) &
(dataframe['macd'].shift(1) >= dataframe['macd_signal'].shift(1))
)
# MACD零轴上方金叉（更强信号）
dataframe['macd_strong_golden'] = dataframe['macd_golden_cross'] & (dataframe['macd'] > 0)
# ==================== MACD动能判断 ====================
# 柱状图动能增加（多头）
dataframe['macd_momentum_up'] = (
(dataframe['macd_hist'] > 0) &
(dataframe['macd_hist'] > dataframe['macd_hist'].shift(1)) &
(dataframe['macd_hist'].shift(1) > dataframe['macd_hist'].shift(2))
)
# 柱状图动能增加（空头）
dataframe['macd_momentum_down'] = (
(dataframe['macd_hist'] < 0) &
(dataframe['macd_hist'] < dataframe['macd_hist'].shift(1)) &
(dataframe['macd_hist'].shift(1) < dataframe['macd_hist'].shift(2))
)
# 柱状图动能减弱（顶部信号）
dataframe['macd_momentum_weakening'] = (
(dataframe['macd_hist'] > 0) &
(dataframe['macd_hist'] < dataframe['macd_hist'].shift(1)) &
(dataframe['macd_hist'].shift(1) < dataframe['macd_hist'].shift(2))
)
# ==================== 趋势阶段（EMA + MACD综合判断）====================
# 趋势启动期：EMA开始排列 + MACD金叉 + 柱状图正向增长
dataframe['trend_start_bull'] = (
dataframe['bull_align_short'] & # 至少短中期排列
(dataframe['macd'] > dataframe['macd_signal']) & # MACD金叉状态
(dataframe['macd_hist'] > 0) & # 柱状图为正
(dataframe['macd_hist'] > dataframe['macd_hist'].shift(1)) & # 动能增加
(spread_abs < dataframe['spread_pct_70']) # 发散度不是最大
)
# 趋势发展期：完整排列 + MACD在零轴上方 + 持续放量
dataframe['trend_develop_bull'] = (
dataframe['bull_align'] & # 完整4线排列
(dataframe['macd'] > 0) & # MACD在零轴上方
(dataframe['macd'] > dataframe['macd_signal']) & # 金叉状态
dataframe['slope_bull_confirm'] & # 斜率确认
(dataframe['spread_change'] > 0) # 发散度扩大
)
# 趋势成熟期：发散度大 + MACD动能减弱
dataframe['trend_mature_bull'] = (
dataframe['bull_align'] &
(dataframe['macd'] > 0) &
dataframe['macd_momentum_weakening'] & # 动能减弱
(spread_abs > dataframe['spread_pct_70']) # 发散度很大
)
# 趋势结束：EMA开始死叉 + MACD死叉
dataframe['trend_end_bull'] = (
(dataframe['ema8'] < dataframe['ema24']) & # EMA8死叉EMA24
(dataframe['macd'] < dataframe['macd_signal']) & # MACD死叉
(dataframe['macd_hist'] < 0) # 柱状图转负
)
# 空头阶段（镜像）
dataframe['trend_start_bear'] = (
dataframe['bear_align_short'] &
(dataframe['macd'] < dataframe['macd_signal']) &
(dataframe['macd_hist'] < 0) &
(dataframe['macd_hist'] < dataframe['macd_hist'].shift(1)) &
(spread_abs < dataframe['spread_pct_70'])
)
dataframe['trend_develop_bear'] = (
dataframe['bear_align'] &
(dataframe['macd'] < 0) &
(dataframe['macd'] < dataframe['macd_signal']) &
dataframe['slope_bear_confirm'] &
(dataframe['spread_change'] < 0)
)
dataframe['trend_end_bear'] = (
(dataframe['ema8'] > dataframe['ema24']) &
(dataframe['macd'] > dataframe['macd_signal']) &
(dataframe['macd_hist'] > 0)
)
# ==================== 趋势强度计算 (0-100) ====================
# 1. 排列得分 (0-25)
bull_align_score = (
(dataframe['ema8'] > dataframe['ema24']).astype(int) +
(dataframe['ema24'] > dataframe['ema72']).astype(int) +
(dataframe['ema72'] > dataframe['ema168']).astype(int) +
(dataframe['close'] > dataframe['ema8']).astype(int)
) * 6.25
bear_align_score = (
(dataframe['ema8'] < dataframe['ema24']).astype(int) +
(dataframe['ema24'] < dataframe['ema72']).astype(int) +
(dataframe['ema72'] < dataframe['ema168']).astype(int) +
(dataframe['close'] < dataframe['ema8']).astype(int)
) * 6.25
dataframe['align_score'] = np.maximum(bull_align_score, bear_align_score)
# 2. 斜率得分 (0-25)
dataframe['slope_score'] = np.clip(np.abs(dataframe['slope_ema24']) * 10, 0, 25)
# 3. MACD动能得分 (0-25) - 替换原来的间距得分
macd_hist_norm = dataframe['macd_hist'].abs() / dataframe['close'] * 1000
dataframe['macd_score'] = np.clip(macd_hist_norm * 5, 0, 25)
# 4. ADX得分 (0-25)
dataframe['adx_score'] = np.clip(dataframe['adx'] - 15, 0, 25)
# 综合强度
dataframe['trend_strength'] = (
dataframe['align_score'] +
dataframe['slope_score'] +
dataframe['macd_score'].fillna(12.5) +
dataframe['adx_score']
)
# ==================== 入场信号 ====================
# EMA金叉
dataframe['ema8_cross_ema24_up'] = (
(dataframe['ema8'] > dataframe['ema24']) &
(dataframe['ema8'].shift(1) <= dataframe['ema24'].shift(1))
)
dataframe['ema8_cross_ema24_down'] = (
(dataframe['ema8'] < dataframe['ema24']) &
(dataframe['ema8'].shift(1) >= dataframe['ema24'].shift(1))
)
# 回踩EMA24反弹
dataframe['pullback_buy'] = (
(dataframe['low'].shift(1) <= dataframe['ema24'].shift(1) * 1.005) &
(dataframe['close'] > dataframe['ema8']) &
(dataframe['close'] > dataframe['open']) &
(dataframe['macd'] > dataframe['macd_signal']) # MACD确认
)
# 突破前高
dataframe['high_break'] = dataframe['close'] > dataframe['high'].rolling(24).max().shift(1)
dataframe['low_break'] = dataframe['close'] < dataframe['low'].rolling(24).min().shift(1)
# ==================== 大周期趋势过滤 ====================
dataframe['big_trend_bull'] = (
(dataframe['ema168'] > dataframe['ema168'].shift(24)) &
(dataframe['close'] > dataframe['ema168'])
)
dataframe['big_trend_bear'] = (
(dataframe['ema168'] < dataframe['ema168'].shift(24)) &
(dataframe['close'] < dataframe['ema168'])
)
return dataframe
dataframe.loc[
(
(dataframe['DOWN_Pattern']) &
(dataframe['DOWN_Confirm']) &
(dataframe['Price_Below_EMA52']) &
(dataframe['MACD_Below_Zero']) &
(dataframe['EMA52_Cross_EMA24_DOWN']) &
(dataframe['strong_trend'])
),
["enter_short", "enter_tag"],
] = (1, "ema_down_trend")
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
if dataframe is None or dataframe.empty:
return dataframe
# ==================== 做多入场1：趋势启动（EMA排列 + MACD确认）====================
# 核心条件：EMA开始多头排列 + MACD金叉 + 动能增加
dataframe.loc[
(
dataframe['bull_align_short'] & # 至少短中期排列（EMA8>24>72）
(dataframe['macd'] > dataframe['macd_signal']) & # MACD金叉
(dataframe['macd_hist'] > 0) & # 柱状图为正
(dataframe['macd_hist'] > dataframe['macd_hist'].shift(1)) & # 动能增加
(dataframe['close'] > dataframe['ema24']) & # 价格在EMA24上方
(dataframe['adx'] > 20) & # 有趋势
(dataframe['slope_ema8'] > 0) & # EMA8向上
(dataframe['volume_ratio'] > 0.7)
),
["enter_long", "enter_tag"],
] = (1, "trend_start")
# ==================== 做多入场2：强势突破（突破前高 + MACD确认）====================
dataframe.loc[
(
dataframe['bull_align'] & # 完整排列
dataframe['high_break'] & # 突破前高
(dataframe['macd'] > dataframe['macd_signal']) & # MACD金叉状态
(dataframe['adx'] > self.min_adx) & # 强趋势
(dataframe['trend_strength'] >= self.min_trend_strength - 10) &
(dataframe['volume_ratio'] > 0.9)
),
["enter_long", "enter_tag"],
] = (1, "bull_breakout")
# ==================== 做多入场3：回踩反弹（趋势中回调）====================
dataframe.loc[
(
dataframe['bull_align'] & # 完整排列
(dataframe['low'].shift(1) <= dataframe['ema24'].shift(1) * 1.01) & # 回踩EMA24附近
(dataframe['close'] > dataframe['open']) & # 阳线
(dataframe['close'] > dataframe['ema8']) & # 收盘价回到EMA8上方
(dataframe['macd'] > 0) & # MACD在零轴上方
(dataframe['macd'] > dataframe['macd_signal']) & # 金叉状态
(dataframe['trend_strength'] >= self.min_trend_strength - 15)
),
["enter_long", "enter_tag"],
] = (1, "bull_pullback")
return dataframe
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
if dataframe is None or dataframe.empty:
return dataframe
# ==================== 多头出场1：趋势结束（EMA死叉 + MACD死叉）====================
dataframe.loc[
dataframe['trend_end_bull'] & # EMA8死叉 + MACD死叉 + 柱状图转负
(dataframe['close'] < dataframe['ema24']), # 价格确认跌破EMA24
["exit_long", "exit_tag"],
] = (1, "trend_end")
# ==================== 多头出场2：趋势反转（完全转为空头）====================
dataframe.loc[
dataframe['bear_align'] & # 完全空头排列
(dataframe['macd'] < dataframe['macd_signal']) & # MACD死叉
(dataframe['macd_hist'] < 0) & # 柱状图为负
(dataframe['close'] < dataframe['ema72']) & # 价格跌破EMA72
(dataframe['close'].shift(1) < dataframe['ema72'].shift(1)), # 连续2根确认
["exit_long", "exit_tag"],
] = (1, "trend_reverse")
# ==================== 空头出场1：趋势结束 ====================
dataframe.loc[
dataframe['trend_end_bear'] &
(dataframe['close'] > dataframe['ema24']),
["exit_short", "exit_tag"],
] = (1, "trend_end")
# ==================== 空头出场2：趋势反转 ====================
dataframe.loc[
dataframe['bull_align'] &
(dataframe['macd'] > dataframe['macd_signal']) &
(dataframe['macd_hist'] > 0) &
(dataframe['close'] > dataframe['ema72']) &
(dataframe['close'].shift(1) > dataframe['ema72'].shift(1)),
["exit_short", "exit_tag"],
] = (1, "trend_reverse")
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
if dataframe is None or dataframe.empty:
return dataframe
dataframe.loc[
(
(dataframe['BUY_END']) |
(dataframe['DOWN_Pattern']) |
(dataframe['EMA52_Cross_EMA24_DOWN'])
),
["exit_long", "exit_tag"],
] = (1, "ema_long_exit")
dataframe.loc[
(
(dataframe['SELL_END']) |
(dataframe['UP_Pattern']) |
(dataframe['EMA52_Cross_EMA24_UP'])
),
["exit_short", "exit_tag"],
] = (1, "ema_short_exit")
return dataframe
def get_ticker_indicator(self):
return int(self.timeframe[:-1])
def get_ticker_indicator(self):
return int(self.timeframe[:-1])