添加ema52显示
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jackyu66git
+63
-44
@@ -23,6 +23,7 @@ logger = logging.getLogger(__name__)
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# freqtrade trade -c ./user_data/Chan/config/ChanLun_BTC_30.json --strategy ChanLun_BTC --strategy-path ./user_data/Chan/strategies
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# freqtrade backtesting -c ./user_data/Chan/config/ChanLun_BTC_30.json --strategy ChanLun_BTC --strategy-path ./user_data/Chan/strategies --timerange=20250901-
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# freqtrade download-data -c ./user_data/Chan/config/ChanLun_BTC_30.json -t 1m --pairs BTC/USDT:USDT --timerange=20250405-
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# freqtrade download-data -c ./user_data/Chan/config/ChanLun_BTC_30.json -t 1m 1h 1d 1M --pairs BTC/USDT --timerange=20170101-
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# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces roi --strategy ChanLun_BTC --strategy-path ./user_data/Chan/strategies -c ./user_data/Chan/config/ChanLun_BTC_30.json -e 200 --timerange=20250201-20250901
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# freqtrade edge -c ./user_data/Chan/config/ChanLun_BTC_30.json --strategy ChanLun_BTC --strategy-path ./user_data/Chan/strategies --timerange 20250721-20250901
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# freqtrade plot-dataframe -c ./user_data/Chan/config/ChanLun_BTC_30.json --strategy ChanLun_BTC --strategy-path ./user_data/Chan/strategies --timerange 20250721-20250901
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@@ -76,36 +77,56 @@ class ChanLun_BTC(IStrategy):
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# 关闭分批止盈/仓位调整
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position_adjustment_enable = False
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startup_candle_count = 2880
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time3 = 3
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time5 = 5
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time15 = 15
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time30 = 30
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time60 = 60
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time2h = 120
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time4h = 240
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time1d = 1440
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startup_candle_count = 1600
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time3m = 3
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time5m = 5
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time10m = 10
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time15m = 15
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time30m = 30
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time_m = [3, 5, 10, 15, 30]
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time1h = 60
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time2h = 2
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time4h = 4
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time6h = 6
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time8h = 8
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time12h = 12
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time16h = 16
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time_h = [2, 4, 6, 8, 12, 16]
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time2d = 2
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time3d = 3
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time1w = 7
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time_d = [2, 3, 7]
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time2M = 2
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time3M = 3
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time_M = [2, 3]
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last_time = datetime.now()
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chan = ChanLun()
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last_order = None
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last_trade = None
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pair = 'BTC/USDT:USDT'
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def informative_pairs(self):
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timeframes = ['1h', '1d', '1M']
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informative_pairs = [(self.pair, tf) for tf in timeframes]
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return informative_pairs
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe = self.add_indicators(dataframe)
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# 仅保留15m(用于BSP)与60m(用于ATR过滤/止损)两个重采样
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dataframe_15 = resample_to_interval(dataframe, self.get_ticker_indicator() * 15)
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dataframe_60 = resample_to_interval(dataframe, self.get_ticker_indicator() * 60)
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# 计算多周期BSP（以15m为基准），并合并到15m数据上
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# 先给重采样帧补指标
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dataframe_15 = self.add_indicators(dataframe_15)
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dataframe_60 = self.add_indicators(dataframe_60)
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# 计算15m BSP
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bsp_15 = self.chan.cal_bsp(dataframe, self.get_ticker_indicator())
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# 合并15m与60m到主DF，生成 resample_*_* 列
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dataframe = resampled_merge(dataframe, dataframe_15)
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dataframe = resampled_merge(dataframe, dataframe_60)
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self.init_dataframes(dataframe)
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return dataframe
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def init_dataframes(self, dataframe_m):
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dataframe_1h = self.dp.get_pair_dataframe(pair=self.pair, timeframe='1h')
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dataframe_1d = self.dp.get_pair_dataframe(pair=self.pair, timeframe='1d')
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dataframe_1M = self.dp.get_pair_dataframe(pair=self.pair, timeframe='1M')
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self.chan.init_dataframes(dataframe_m, dataframe_1h, dataframe_1d, dataframe_1M)
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self.print_all_ema52()
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def print_all_ema52(self):
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for key, value in self.chan.get_ema52_dict().items():
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print(key, value)
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def print_all_ema24(self):
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for key, value in self.chan.get_ema24_dict().items():
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print(key, value)
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def add_indicators(self, df):
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fast = 12
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slow = 26
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@@ -212,9 +233,9 @@ class ChanLun_BTC(IStrategy):
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return -0.05
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dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
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last_candle = dataframe.iloc[-1].squeeze()
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ema52_str = 'resample_{}_ema52'.format(self.get_ticker_indicator()*self.time15)
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ema52_str = 'resample_{}_ema52'.format(self.time15m)
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ema52_val = float(last_candle.get(ema52_str, 0) or 0)
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close_str = 'resample_{}_close'.format(self.get_ticker_indicator()*self.time15)
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close_str = 'resample_{}_close'.format(self.time15m)
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close_val = float(last_candle.get(close_str, 0) or 0)
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if close_val < ema52_val:
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return -0.01
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@@ -240,7 +261,7 @@ class ChanLun_BTC(IStrategy):
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if dataframe is None or len(dataframe) == 0:
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return False
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last = dataframe.iloc[-1]
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atr_str = 'resample_{}_atr'.format(self.get_ticker_indicator()*self.time60)
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atr_str = 'resample_{}_atr'.format(self.time1h)
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atr_val = float(last.get(atr_str, 0) or 0)
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if atr_val < 0.001:
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#logger.info(f"ATR过滤：atr={atr_val:.2f} < 100, 拒绝进场 {pair}")
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@@ -263,7 +284,7 @@ class ChanLun_BTC(IStrategy):
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# Obtain pair dataframe (just to show how to access it)
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dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
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last_candle = dataframe.iloc[-1].squeeze()
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atr_str = 'resample_{}_atr'.format(self.get_ticker_indicator()*self.time15)
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atr_str = 'resample_{}_atr'.format(elf.time15)
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# 保存开仓时的ATR值用于止损计算
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if (trade.nr_of_successful_entries == 1) and (order.ft_order_side == trade.entry_side):
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entry_atr = last_candle[atr_str] * 4
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@@ -271,13 +292,13 @@ class ChanLun_BTC(IStrategy):
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#logger.info(f"保存开仓时ATR值: {entry_atr}")
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return None
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def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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shift15 = self.time15
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shift60 = self.time60
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bsp_col = 'resample_{}_bsp_mtf'.format(self.get_ticker_indicator()*shift15)
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score_col = 'resample_{}_mtf_score'.format(self.get_ticker_indicator()*shift15)
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macdh_col = 'resample_{}_macdhist'.format(self.get_ticker_indicator()*shift15)
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c60_col = 'resample_{}_close'.format(self.get_ticker_indicator()*shift60)
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e60_col = 'resample_{}_ema52'.format(self.get_ticker_indicator()*shift60)
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shift15 = self.time15m
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shift60 = self.time1h
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bsp_col = 'resample_{}_bsp_mtf'.format(shift15)
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score_col = 'resample_{}_mtf_score'.format(shift15)
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macdh_col = 'resample_{}_macdhist'.format(shift15)
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c60_col = 'resample_{}_close'.format(shift60)
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e60_col = 'resample_{}_ema52'.format(shift60)
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# 强化过滤：15m BSP + 分数阈值 + 60m 趋势同向 + 15m MACD柱同向
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if all(col in dataframe.columns for col in [bsp_col, score_col, macdh_col, c60_col, e60_col]):
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dataframe.loc[
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@@ -298,12 +319,12 @@ class ChanLun_BTC(IStrategy):
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['enter_short', 'enter_tag']] = (1, 'short_bsp15_v2')
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return dataframe
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def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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shift15 = self.time15
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shift60 = self.time60
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bsp_col = 'resample_{}_bsp_mtf'.format(self.get_ticker_indicator()*shift15)
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score_col = 'resample_{}_mtf_score'.format(self.get_ticker_indicator()*shift15)
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c60_col = 'resample_{}_close'.format(self.get_ticker_indicator()*shift60)
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e60_col = 'resample_{}_ema52'.format(self.get_ticker_indicator()*shift60)
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shift15 = self.time15m
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shift60 = self.time1h
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bsp_col = 'resample_{}_bsp_mtf'.format(shift15)
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score_col = 'resample_{}_mtf_score'.format(shift15)
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c60_col = 'resample_{}_close'.format(shift60)
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e60_col = 'resample_{}_ema52'.format(shift60)
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# 反向强信号或60m趋势反向时平仓
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if all(col in dataframe.columns for col in [bsp_col, score_col, c60_col, e60_col]):
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dataframe.loc[
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@@ -323,6 +344,4 @@ class ChanLun_BTC(IStrategy):
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proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str,
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**kwargs) -> float:
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return self.lev
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def get_ticker_indicator(self):
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return int(self.timeframe[:-1])
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