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加入熊牛趋势检查机制, 趋势低于60分禁止入场

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zhangkun9038@dingtalk.com 2025-08-30 12:49:38 +08:00
parent a7311b72c6
commit 6aeb28f32b
1 changed files with 174 additions and 19 deletions
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freqtrade/templates/freqaiprimer.py
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@ -1,9 +1,15 @@
import warnings
warnings.filterwarnings("ignore", category=UserWarning, module="pandas_ta")
import logging
from freqtrade.strategy import IStrategy
from pandas import DataFrame
import pandas_ta as ta
from freqtrade.persistence import Trade
import numpy as np
import datetime
logger = logging.getLogger(__name__)
class FreqaiPrimer(IStrategy):
# 策略参数
@ -28,6 +34,11 @@ class FreqaiPrimer(IStrategy):
rsi_length = 14
rsi_overbought = 58 # 收紧超买阈值到 60
rsi_oversold = 39 # 放宽超卖阈值到 45
# 剧烈拉升检测参数
H1_MAX_CANDLES = 200 # 检查最近200根1h K线
H1_RAPID_RISE_THRESHOLD = 0.05 # 5%的抬升阈值
H1_MAX_CONSECUTIVE_CANDLES = 3 # 最多连续3根K线内检查
def informative_pairs(self):
pairs = self.dp.current_whitelist()
@ -39,7 +50,11 @@ class FreqaiPrimer(IStrategy):
dataframe['bb_lower_3m'] = bb_3m[f'BBL_{self.bb_length}_{self.bb_std}']
dataframe['bb_upper_3m'] = bb_3m[f'BBU_{self.bb_length}_{self.bb_std}']
dataframe['rsi_3m'] = ta.rsi(dataframe['close'], length=self.rsi_length)
# 计算3m时间框架的EMA50和EMA200
dataframe['ema_50_3m'] = ta.ema(dataframe['close'], length=50)
dataframe['ema_200_3m'] = ta.ema(dataframe['close'], length=200)
# 成交量过滤
dataframe['volume_ma'] = dataframe['volume'].rolling(20).mean()
@ -49,9 +64,12 @@ class FreqaiPrimer(IStrategy):
# 获取 15m 数据
df_15m = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='15m')
df_15m['rsi_15m'] = ta.rsi(df_15m['close'], length=self.rsi_length)
# 计算15m时间框架的EMA50和EMA200
df_15m['ema_50_15m'] = ta.ema(df_15m['close'], length=50)
df_15m['ema_200_15m'] = ta.ema(df_15m['close'], length=200)
# 手动合并 15m 数据
df_15m = df_15m[['date', 'rsi_15m']]
df_15m = df_15m[['date', 'rsi_15m', 'ema_50_15m', 'ema_200_15m']]
df_15m = df_15m.rename(columns={'date': 'date_15m'})
dataframe = dataframe.merge(df_15m, how='left', left_on='date', right_on='date_15m')
dataframe = dataframe.fillna(method='ffill')
@ -60,10 +78,11 @@ class FreqaiPrimer(IStrategy):
df_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='1h')
df_1h['rsi_1h'] = ta.rsi(df_1h['close'], length=self.rsi_length)
df_1h['ema_50_1h'] = ta.ema(df_1h['close'], length=50) # 1h 50周期EMA
df_1h['ema_200_1h'] = ta.ema(df_1h['close'], length=200) # 1h 200周期EMA
df_1h['trend_1h'] = df_1h['close'] > df_1h['ema_50_1h'] # 1h上涨趋势
# 手动合并 1h 数据
df_1h = df_1h[['date', 'rsi_1h', 'trend_1h']]
df_1h = df_1h[['date', 'rsi_1h', 'trend_1h', 'ema_50_1h', 'ema_200_1h']]
df_1h = df_1h.rename(columns={'date': 'date_1h'})
dataframe = dataframe.merge(df_1h, how='left', left_on='date', right_on='date_1h')
dataframe = dataframe.fillna(method='ffill')
@ -76,34 +95,105 @@ class FreqaiPrimer(IStrategy):
(dataframe['open'] < dataframe['close'].shift(1))
)
# 计算各时间框架的趋势状态（牛/熊）
# 3m时间框架：ema50下穿ema200为熊，上穿为牛
dataframe['trend_3m'] = np.where(dataframe['ema_50_3m'] > dataframe['ema_200_3m'], 1, 0)
# 15m时间框架：ema50下穿ema200为熊，上穿为牛
dataframe['trend_15m'] = np.where(dataframe['ema_50_15m'] > dataframe['ema_200_15m'], 1, 0)
# 1h时间框架：ema50下穿ema200为熊，上穿为牛
dataframe['trend_1h_ema'] = np.where(dataframe['ema_50_1h'] > dataframe['ema_200_1h'], 1, 0)
# 计算熊牛得分（0-100）
# 权重：3m熊牛权重10，15m熊牛权重35，1h熊牛权重65
# 计算加权得分
dataframe['market_score'] = (
dataframe['trend_3m'] * 10 +
dataframe['trend_15m'] * 35 +
dataframe['trend_1h_ema'] * 65
)
# 确保得分在0-100范围内
dataframe['market_score'] = dataframe['market_score'].clip(lower=0, upper=100)
# 根据得分分类市场状态
dataframe['market_state'] = 'neutral'
dataframe.loc[dataframe['market_score'] > 70, 'market_state'] = 'strong_bull'
dataframe.loc[(dataframe['market_score'] > 50) & (dataframe['market_score'] <= 70), 'market_state'] = 'weak_bull'
dataframe.loc[(dataframe['market_score'] >= 30) & (dataframe['market_score'] <= 50), 'market_state'] = 'neutral'
dataframe.loc[(dataframe['market_score'] > 10) & (dataframe['market_score'] < 30), 'market_state'] = 'weak_bear'
dataframe.loc[dataframe['market_score'] <= 10, 'market_state'] = 'strong_bear'
# 记录当前的市场状态
if len(dataframe) > 0:
current_score = dataframe['market_score'].iloc[-1]
current_state = dataframe['market_state'].iloc[-1]
logger.info(f"[{metadata['pair']}] 熊牛得分: {current_score:.1f}, 市场状态: {current_state}")
logger.info(f"[{metadata['pair']}] 各时间框架趋势: 3m={'牛' if dataframe['trend_3m'].iloc[-1] == 1 else '熊'}, \
15m={'牛' if dataframe['trend_15m'].iloc[-1] == 1 else '熊'}, \
1h={'牛' if dataframe['trend_1h_ema'].iloc[-1] == 1 else '熊'}")
# 调试：打印指标值（最后 5 行）
print(f"Pair: {metadata['pair']}, Last 5 rows:")
print(dataframe[['date', 'close', 'bb_lower_3m', 'rsi_3m', 'rsi_15m', 'rsi_1h', 'trend_1h', 'bullish_engulfing', 'volume', 'volume_ma']].tail(5))
print(dataframe[['date', 'close', 'bb_lower_3m', 'rsi_3m', 'rsi_15m', 'rsi_1h', 'trend_1h',
'trend_3m', 'trend_15m', 'trend_1h_ema', 'market_score', 'market_state',
'bullish_engulfing', 'volume', 'volume_ma']].tail(5))
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# 做多条件（放宽以达到每月150单）
conditions = [
(dataframe['close'] <= dataframe['bb_lower_3m'] * 1.03), # 价格接近3m布林带下轨（3%偏差）
(dataframe['rsi_3m'] < self.rsi_oversold), # 3m RSI < 45
(dataframe['rsi_15m'] < self.rsi_oversold), # 15m RSI < 45
(dataframe['trend_1h']), # 1h上涨趋势
(dataframe['volume'] > dataframe['volume_ma']) # 成交量高于均量
]
# 看涨吞没或极低RSI（可选）
conditions.append(dataframe['bullish_engulfing'] | (dataframe['rsi_3m'] < self.rsi_oversold - 5))
dataframe.loc[conditions[0] & conditions[1] & conditions[2] & conditions[3] & conditions[4] & conditions[5], 'enter_long'] = 1
# 1. 价格接近3m布林带下轨（3%偏差）
close_to_bb_lower = (dataframe['close'] <= dataframe['bb_lower_3m'] * 1.03)
# 2. 多时间框架RSI处于超卖区域
rsi_oversold = (dataframe['rsi_3m'] < self.rsi_oversold) & \
(dataframe['rsi_15m'] < self.rsi_oversold)
# 3. 1小时趋势向上
trend_1h = dataframe['trend_1h']
# 4. 成交量高于平均水平
volume_condition = dataframe['volume'] > dataframe['volume_ma']
# 5. 看涨吞没形态或极低RSI
special_condition = dataframe['bullish_engulfing'] | (dataframe['rsi_3m'] < self.rsi_oversold - 5)
# 检查剧烈拉升情况 - 如果检测到剧烈拉升，则不产生入场信号
pair = metadata['pair']
is_unstable_region, _ = self.detect_h1_rapid_rise(pair, dataframe, metadata)
# 熊牛得分检查：当得分低于60时禁止入场
market_score_condition = dataframe['market_score'] >= 60
# 合并所有条件，并且确保不在不稳固区域且市场状态良好
final_condition = close_to_bb_lower & rsi_oversold & trend_1h & volume_condition & special_condition & (~is_unstable_region) & market_score_condition
# 所有原始条件（不包括剧烈拉升检查和熊牛得分检查）
original_conditions = close_to_bb_lower & rsi_oversold & trend_1h & volume_condition & special_condition & (~is_unstable_region)
dataframe.loc[final_condition, 'enter_long'] = 1
# 记录入场信号被阻止的情况
prevented_entries = dataframe[(~final_condition) & original_conditions]
# 统计因熊牛得分不足而被阻止的信号
low_score_prevented = dataframe[(~market_score_condition) & original_conditions]
if len(low_score_prevented) > 0:
logger.info(f"[{pair}] 由于熊牛得分低于60，阻止了 {len(low_score_prevented)} 个潜在的入场信号")
if len(prevented_entries) > 0:
logger.info(f"[{pair}] 总共阻止了 {len(prevented_entries)} 个潜在的入场信号")
# 调试：检查每个条件的触发情况
print(f"Pair: {metadata['pair']}, Entry condition checks:")
print(f" - Close <= bb_lower_3m * 1.03: {(dataframe['close'] <= dataframe['bb_lower_3m'] * 1.03).sum()} candles")
print(f" - Close <= bb_lower_3m * 1.03: {close_to_bb_lower.sum()} candles")
print(f" - RSI_3m < {self.rsi_oversold}: {(dataframe['rsi_3m'] < self.rsi_oversold).sum()} candles")
print(f" - RSI_15m < {self.rsi_oversold}: {(dataframe['rsi_15m'] < self.rsi_oversold).sum()} candles")
print(f" - Trend_1h (Close > EMA_50_1h): {(dataframe['trend_1h']).sum()} candles")
print(f" - Volume > Volume_MA: {(dataframe['volume'] > dataframe['volume_ma']).sum()} candles")
print(f" - Bullish Engulfing or RSI_3m < {self.rsi_oversold - 5}: {(dataframe['bullish_engulfing'] | (dataframe['rsi_3m'] < self.rsi_oversold - 5)).sum()} candles")
print(f" - Trend_1h (Close > EMA_50_1h): {trend_1h.sum()} candles")
print(f" - Volume > Volume_MA: {volume_condition.sum()} candles")
print(f" - Bullish Engulfing or RSI_3m < {self.rsi_oversold - 5}: {special_condition.sum()} candles")
if dataframe['enter_long'].sum() > 0:
print(f"Entry signals found at:")
print(dataframe[dataframe['enter_long'] == 1][['date', 'close', 'rsi_3m', 'rsi_15m', 'trend_1h', 'bullish_engulfing']])
@ -123,6 +213,71 @@ class FreqaiPrimer(IStrategy):
'exit_long'] = 1
return dataframe
def detect_h1_rapid_rise(self, pair: str, dataframe: DataFrame, metadata: dict) -> tuple[bool, float]:
"""
检测1小时K线图上的剧烈拉升情况
参数:
- pair: 交易对
- dataframe: 数据框
- metadata: 元数据
返回:
- tuple: (是否处于不稳固区域, 当前价格在最近价格范围中的百分比)
"""
try:
# 获取1小时K线数据
df_1h = self.dp.get_pair_dataframe(pair=pair, timeframe='1h')
# 确保有足够的K线数据
if len(df_1h) < self.H1_MAX_CANDLES:
logger.warning(f"[{pair}] 1h K线数据不足 {self.H1_MAX_CANDLES} 根，当前只有 {len(df_1h)} 根，无法完整检测剧烈拉升")
return False, 0.0
# 获取最近的K线
recent_data = df_1h.iloc[-self.H1_MAX_CANDLES:].copy()
current_price = recent_data['close'].iloc[-1]
# 检查连续最多3根K线内的最大涨幅
rapid_rise_detected = False
max_rise = 0
for i in range(len(recent_data) - self.H1_MAX_CONSECUTIVE_CANDLES + 1):
window_data = recent_data.iloc[i:i + self.H1_MAX_CONSECUTIVE_CANDLES]
window_low = window_data['low'].min()
window_high = window_data['high'].max()
# 计算区间内的最大涨幅
if window_low > 0:
rise_percentage = (window_high - window_low) / window_low
if rise_percentage > max_rise:
max_rise = rise_percentage
# 检查是否超过阈值
if rise_percentage >= self.H1_RAPID_RISE_THRESHOLD:
rapid_rise_detected = True
logger.info(f"[{pair}] 检测到剧烈拉升: 从 {window_low:.2f} 到 {window_high:.2f} ({rise_percentage:.2%}) 在 {self.H1_MAX_CONSECUTIVE_CANDLES} 根K线内")
break
# 计算当前价格在最近价格范围中的位置百分比
recent_low = recent_data['low'].min()
recent_high = recent_data['high'].max()
if recent_high > recent_low:
price_position_pct = (current_price - recent_low) / (recent_high - recent_low) * 100
else:
price_position_pct = 50.0 # 默认中间位置
# 判断是否处于不稳固区域
is_unstable_region = rapid_rise_detected
logger.info(f"[{pair}] 剧烈拉升检测结果: {'不稳固' if is_unstable_region else '稳固'}")
logger.info(f"[{pair}] 当前价格位置: {current_price:.2f} ({price_position_pct:.1f}%), 最近最大涨幅: {max_rise:.2%}")
return is_unstable_region, price_position_pct
except Exception as e:
logger.error(f"[{pair}] 剧烈拉升检测过程中发生错误: {str(e)}")
return False, 0.0
def custom_stoploss(self, pair: str, trade: 'Trade', current_time, current_rate: float,
current_profit: float, **kwargs) -> float:
# 动态止损基于ATR