A new strategy is added haha
This commit is contained in:
jackyu66git
+674
-3
@@ -30,6 +30,7 @@ class ChanKLC():
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self.klc_fx_type = Chan_KLC_FX.UNKNOWN
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self.rsi = klu.rsi
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self.volume_ratio = klu.volume_ratio
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self.macdhist = 0
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def set_klc_fx_type(self, klc_fx_type):
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#print(self.start_time, klc_fx_type, self.get_feature_data()['klu_macd'], self.get_feature_data()['klu_macdhist'], self.get_feature_data()['klu_rsi'])
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self.klc_fx_type = klc_fx_type
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@@ -43,8 +44,11 @@ class ChanKLC():
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self.volume += self.klus[index].volume
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self.rsi += self.klus[index].rsi
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self.volume_ratio += self.klus[index].volume_ratio
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self.macdhist += self.klus[index].macdhist
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self.rsi = self.rsi / len(self.klus)
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self.volume_ratio = self.volume_ratio / len(self.klus)
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self.volume = self.volume / len(self.klus)
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self.macdhist = self.macdhist / len(self.klus)
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def set_next(self, klc):
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self.next = klc
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def set_pre(self, klc):
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@@ -122,6 +126,7 @@ class ChanKLC():
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def set_bi(self, bi):
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self.bi = bi
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self.distance = self.index - bi.start_klc.index
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#print(self.start_time, self.distance, bi.index, bi.dir)
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def cal_klu_features(self):
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features = dict()
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feature_sums = dict()
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@@ -189,7 +194,7 @@ class ChanKLC():
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# K线波动范围
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if self.close != 0: # 避免除以零
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features['klc_range'] = (self.high - self.low) / self.close
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features['klc_range'] = 0 #(self.high - self.low) / self.close
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else:
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features['klc_range'] = 0
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@@ -301,9 +306,9 @@ class ChanKLC():
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features['klc_macd_signal'] = 0
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if 'klu_macdhist' in klu_features:
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features['klc_macd_hist'] = klu_features['klu_macdhist']
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features['klc_macdhist'] = klu_features['klu_macdhist']
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else:
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features['klc_macd_hist'] = 0
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features['klc_macdhist'] = 0
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# 成交量变化
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if self.pre:
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@@ -459,4 +464,670 @@ class ChanKLC():
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# 从KLU获取其他特征
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#features.update(self.cal_klu_features())
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# ===== 3.1 价格形态扩展因子 =====
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# 区间突破强度
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if self.pre and self.pre.pre:
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prev_range = self.pre.high - self.pre.low
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if prev_range > 0:
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features['klc_breakout_strength'] = (self.close - self.pre.high) / prev_range if self.close > self.pre.high else (self.pre.low - self.close) / prev_range if self.close < self.pre.low else 0
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else:
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features['klc_breakout_strength'] = 0
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else:
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features['klc_breakout_strength'] = 0
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# 价格动量
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if self.pre:
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features['klc_momentum_1'] = self.close - self.pre.close
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if self.pre.pre:
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features['klc_momentum_2'] = self.close - self.pre.pre.close
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else:
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features['klc_momentum_2'] = 0
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else:
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features['klc_momentum_1'] = 0
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features['klc_momentum_2'] = 0
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# 价格加速度
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if self.pre and self.pre.pre:
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prev_change = self.pre.close - self.pre.pre.close
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curr_change = self.close - self.pre.close
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features['klc_price_acceleration'] = curr_change - prev_change
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else:
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features['klc_price_acceleration'] = 0
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# 相对位置
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if self.high != self.low:
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features['klc_relative_position'] = (self.close - self.low) / (self.high - self.low)
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else:
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features['klc_relative_position'] = 0.5
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# 价格区间位置 (前N根K线)
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prev_klcs = []
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temp = self.pre
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for _ in range(10): # 前10根K线
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if temp:
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prev_klcs.append(temp)
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temp = temp.pre
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else:
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break
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if prev_klcs:
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max_high = max([klc.high for klc in prev_klcs]) if prev_klcs else self.high
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min_low = min([klc.low for klc in prev_klcs]) if prev_klcs else self.low
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price_range = max_high - min_low
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if price_range > 0:
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features['klc_range_position'] = (self.close - min_low) / price_range
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else:
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features['klc_range_position'] = 0.5
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else:
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features['klc_range_position'] = 0.5
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# ===== 3.2 更多技术指标因子 =====
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# MACD趋势
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if self.pre and 'klc_macdhist' in features:
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features['klc_macdhist_change'] = features['klc_macdhist'] - self.pre.macdhist
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else:
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features['klc_macdhist_change'] = 0
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# RSI趋势
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if self.pre and 'klc_rsi' in features:
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features['klc_rsi_change'] = features['klc_rsi'] - self.pre.rsi
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else:
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features['klc_rsi_change'] = 0
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# RSI超买超卖
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if 'klc_rsi' in features:
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features['klc_rsi_overbought'] = 1 if features['klc_rsi'] > 70 else 0
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features['klc_rsi_oversold'] = 1 if features['klc_rsi'] < 30 else 0
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else:
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features['klc_rsi_overbought'] = 0
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features['klc_rsi_oversold'] = 0
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# 布林带位置 (如果可从KLU获取)
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if 'klu_upper_band' in klu_features and 'klu_lower_band' in klu_features:
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upper_band = klu_features['klu_upper_band']
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lower_band = klu_features['klu_lower_band']
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middle_band = klu_features['klu_middle_band'] if 'klu_middle_band' in klu_features else (upper_band + lower_band) / 2
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band_width = upper_band - lower_band
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if band_width > 0:
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features['klc_bollinger_position'] = (self.close - lower_band) / band_width
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else:
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features['klc_bollinger_position'] = 0.5
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features['klc_bollinger_width'] = band_width / middle_band if middle_band > 0 else 0
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features['klc_upper_band_touch'] = 1 if self.high >= upper_band else 0
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features['klc_lower_band_touch'] = 1 if self.low <= lower_band else 0
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else:
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features['klc_bollinger_position'] = 0.5
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features['klc_bollinger_width'] = 0
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features['klc_upper_band_touch'] = 0
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features['klc_lower_band_touch'] = 0
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# 量价关系
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if self.pre:
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price_change = self.close - self.pre.close
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if price_change != 0:
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features['klc_volume_price_ratio'] = self.volume / abs(price_change)
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else:
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features['klc_volume_price_ratio'] = 0
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else:
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features['klc_volume_price_ratio'] = 0
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# ===== 3.3 波动性因子 =====
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# 真实波动幅度 (True Range)
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if self.pre:
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tr1 = self.high - self.low
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tr2 = abs(self.high - self.pre.close)
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tr3 = abs(self.low - self.pre.close)
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features['klc_true_range'] = max(tr1, tr2, tr3)
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else:
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features['klc_true_range'] = self.high - self.low
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# 归一化真实波动幅度
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if self.pre and self.pre.close > 0:
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features['klc_normalized_tr'] = features['klc_true_range'] / self.pre.close
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else:
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features['klc_normalized_tr'] = 0
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# 滑动窗口波动率
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if prev_klcs:
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tr_values = []
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for i in range(len(prev_klcs)):
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if i == 0:
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tr = max(prev_klcs[i].high - prev_klcs[i].low,
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abs(prev_klcs[i].high - self.close),
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abs(prev_klcs[i].low - self.close))
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else:
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tr = max(prev_klcs[i].high - prev_klcs[i].low,
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abs(prev_klcs[i].high - prev_klcs[i-1].close),
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abs(prev_klcs[i].low - prev_klcs[i-1].close))
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tr_values.append(tr)
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if tr_values:
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import numpy as np
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# ATR (Average True Range)
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features['klc_atr'] = np.mean(tr_values)
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if self.close > 0:
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features['klc_atr_percent'] = features['klc_atr'] / self.close
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else:
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features['klc_atr_percent'] = 0
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# 高低点波动
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if len(prev_klcs) >= 5:
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highs = [klc.high for klc in prev_klcs[:5]]
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lows = [klc.low for klc in prev_klcs[:5]]
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max_high = max(highs)
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min_low = min(lows)
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features['klc_high_volatility'] = np.std(highs) / np.mean(highs) if np.mean(highs) > 0 else 0
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features['klc_low_volatility'] = np.std(lows) / np.mean(lows) if np.mean(lows) > 0 else 0
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features['klc_price_range'] = (max_high - min_low) / min_low if min_low > 0 else 0
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else:
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features['klc_high_volatility'] = 0
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features['klc_low_volatility'] = 0
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features['klc_price_range'] = 0
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else:
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features['klc_atr'] = 0
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features['klc_atr_percent'] = 0
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features['klc_high_volatility'] = 0
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features['klc_low_volatility'] = 0
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features['klc_price_range'] = 0
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else:
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features['klc_atr'] = 0
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features['klc_atr_percent'] = 0
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features['klc_high_volatility'] = 0
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features['klc_low_volatility'] = 0
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features['klc_price_range'] = 0
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# ===== 3.4 趋势强度因子 =====
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# 方向移动指标
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if self.pre:
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# 上升动量和下降动量
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up_move = self.high - self.pre.high
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down_move = self.pre.low - self.low
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features['klc_plus_dm'] = up_move if up_move > down_move and up_move > 0 else 0
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features['klc_minus_dm'] = down_move if down_move > up_move and down_move > 0 else 0
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# 方向指数
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if features['klc_atr'] > 0:
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features['klc_plus_di'] = 100 * features['klc_plus_dm'] / features['klc_atr']
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features['klc_minus_di'] = 100 * features['klc_minus_dm'] / features['klc_atr']
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else:
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features['klc_plus_di'] = 0
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features['klc_minus_di'] = 0
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# 方向指数差
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features['klc_dx'] = 100 * abs(features['klc_plus_di'] - features['klc_minus_di']) / (features['klc_plus_di'] + features['klc_minus_di']) if (features['klc_plus_di'] + features['klc_minus_di']) > 0 else 0
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else:
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features['klc_plus_dm'] = 0
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features['klc_minus_dm'] = 0
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features['klc_plus_di'] = 0
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features['klc_minus_di'] = 0
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features['klc_dx'] = 0
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# 价格趋势强度
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if prev_klcs and len(prev_klcs) >= 5:
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import numpy as np
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prices = [self.close] + [klc.close for klc in prev_klcs[:5]]
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x = np.arange(len(prices))
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# 线性回归
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slope, intercept = np.polyfit(x, prices, 1)
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# 趋势线拟合度 (R^2)
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y_pred = slope * x + intercept
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ss_total = np.sum((prices - np.mean(prices)) ** 2)
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ss_residual = np.sum((prices - y_pred) ** 2)
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if ss_total > 0:
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features['klc_trend_r2'] = 1 - (ss_residual / ss_total)
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else:
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features['klc_trend_r2'] = 0
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# 趋势线斜率
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features['klc_trend_slope_norm'] = slope / np.mean(prices) if np.mean(prices) > 0 else 0
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# 价格与趋势线的距离
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current_trend_value = slope * 0 + intercept # x=0 表示当前K线在预测线上的值
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if current_trend_value > 0:
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features['klc_trend_distance'] = (self.close - current_trend_value) / current_trend_value
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else:
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features['klc_trend_distance'] = 0
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else:
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features['klc_trend_r2'] = 0
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features['klc_trend_slope_norm'] = 0
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features['klc_trend_distance'] = 0
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# ===== 3.5 支撑与阻力因子 =====
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# 前N根K线的支撑和阻力
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if prev_klcs and len(prev_klcs) >= 5:
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highs = [klc.high for klc in prev_klcs[:5]]
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lows = [klc.low for klc in prev_klcs[:5]]
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# 简单支撑位 (前5根K线最低点)
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support = min(lows)
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# 简单阻力位 (前5根K线最高点)
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resistance = max(highs)
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# 与支撑阻力的距离
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if support > 0:
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features['klc_distance_to_support'] = (self.close - support) / support
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else:
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features['klc_distance_to_support'] = 0
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if resistance > 0:
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features['klc_distance_to_resistance'] = (resistance - self.close) / resistance
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else:
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features['klc_distance_to_resistance'] = 0
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# 支撑阻力突破
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features['klc_breaks_support'] = 1 if self.low < support else 0
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features['klc_breaks_resistance'] = 1 if self.high > resistance else 0
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# 支撑阻力区间位置
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if resistance > support:
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features['klc_sr_position'] = (self.close - support) / (resistance - support)
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else:
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features['klc_sr_position'] = 0.5
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else:
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features['klc_distance_to_support'] = 0
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features['klc_distance_to_resistance'] = 0
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features['klc_breaks_support'] = 0
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features['klc_breaks_resistance'] = 0
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features['klc_sr_position'] = 0.5
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# ===== 3.6 量价关系扩展因子 =====
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# 价格与成交量的相关性
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if prev_klcs and len(prev_klcs) >= 5:
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import numpy as np
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prices = [self.close] + [klc.close for klc in prev_klcs[:5]]
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volumes = [self.volume] + [klc.volume for klc in prev_klcs[:5]]
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# 计算相关系数
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if len(prices) > 1 and np.std(prices) > 0 and np.std(volumes) > 0:
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price_mean = np.mean(prices)
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volume_mean = np.mean(volumes)
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numerator = np.sum((prices - price_mean) * (volumes - volume_mean))
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denominator = np.sqrt(np.sum((prices - price_mean) ** 2) * np.sum((volumes - volume_mean) ** 2))
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if denominator > 0:
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features['klc_price_volume_corr'] = numerator / denominator
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else:
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features['klc_price_volume_corr'] = 0
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else:
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features['klc_price_volume_corr'] = 0
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# 价格上涨时的平均成交量
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up_prices = []
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up_volumes = []
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# 价格下跌时的平均成交量
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down_prices = []
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down_volumes = []
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for i in range(len(prev_klcs)):
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if i < len(prev_klcs) - 1:
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if prev_klcs[i].close > prev_klcs[i+1].close:
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up_prices.append(prev_klcs[i].close)
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up_volumes.append(prev_klcs[i].volume)
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else:
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down_prices.append(prev_klcs[i].close)
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down_volumes.append(prev_klcs[i].volume)
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features['klc_up_volume_avg'] = np.mean(up_volumes) if up_volumes else 0
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features['klc_down_volume_avg'] = np.mean(down_volumes) if down_volumes else 0
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if features['klc_down_volume_avg'] > 0:
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features['klc_volume_ratio_up_down'] = features['klc_up_volume_avg'] / features['klc_down_volume_avg']
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else:
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features['klc_volume_ratio_up_down'] = 1
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else:
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features['klc_price_volume_corr'] = 0
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features['klc_up_volume_avg'] = 0
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features['klc_down_volume_avg'] = 0
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features['klc_volume_ratio_up_down'] = 1
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# 成交量变化率
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if self.pre:
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if self.pre.volume > 0:
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features['klc_volume_change'] = (self.volume - self.pre.volume) / self.pre.volume
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else:
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features['klc_volume_change'] = 0
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else:
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features['klc_volume_change'] = 0
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# 量能扩散
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if prev_klcs and len(prev_klcs) >= 5:
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avg_volume = np.mean([klc.volume for klc in prev_klcs[:5]])
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if avg_volume > 0:
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features['klc_volume_expansion'] = self.volume / avg_volume
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else:
|
||||
features['klc_volume_expansion'] = 1
|
||||
else:
|
||||
features['klc_volume_expansion'] = 1
|
||||
|
||||
# ===== 3.7 K线时序模式因子 =====
|
||||
|
||||
# 连续上涨/下跌计数
|
||||
up_count = 0
|
||||
down_count = 0
|
||||
|
||||
if prev_klcs:
|
||||
temp = self
|
||||
last_close = temp.close
|
||||
|
||||
for klc in prev_klcs:
|
||||
if klc.close < last_close:
|
||||
up_count += 1
|
||||
down_count = 0
|
||||
elif klc.close > last_close:
|
||||
down_count += 1
|
||||
up_count = 0
|
||||
last_close = klc.close
|
||||
|
||||
features['klc_consecutive_up'] = up_count
|
||||
features['klc_consecutive_down'] = down_count
|
||||
else:
|
||||
features['klc_consecutive_up'] = 0
|
||||
features['klc_consecutive_down'] = 0
|
||||
|
||||
# 跳空缺口
|
||||
if self.pre:
|
||||
features['klc_gap_up'] = self.low - self.pre.high if self.low > self.pre.high else 0
|
||||
features['klc_gap_down'] = self.pre.low - self.high if self.high < self.pre.low else 0
|
||||
|
||||
# 归一化缺口大小
|
||||
if self.pre.close > 0:
|
||||
features['klc_gap_up_pct'] = features['klc_gap_up'] / self.pre.close
|
||||
features['klc_gap_down_pct'] = features['klc_gap_down'] / self.pre.close
|
||||
else:
|
||||
features['klc_gap_up_pct'] = 0
|
||||
features['klc_gap_down_pct'] = 0
|
||||
else:
|
||||
features['klc_gap_up'] = 0
|
||||
features['klc_gap_down'] = 0
|
||||
features['klc_gap_up_pct'] = 0
|
||||
features['klc_gap_down_pct'] = 0
|
||||
|
||||
# 价格回撤
|
||||
if prev_klcs:
|
||||
max_price = self.close
|
||||
min_price = self.close
|
||||
|
||||
for klc in prev_klcs[:5]:
|
||||
max_price = max(max_price, klc.close)
|
||||
min_price = min(min_price, klc.close)
|
||||
|
||||
if max_price > 0:
|
||||
features['klc_drawdown'] = (max_price - self.close) / max_price
|
||||
else:
|
||||
features['klc_drawdown'] = 0
|
||||
|
||||
if min_price > 0:
|
||||
features['klc_pullback'] = (self.close - min_price) / min_price
|
||||
else:
|
||||
features['klc_pullback'] = 0
|
||||
else:
|
||||
features['klc_drawdown'] = 0
|
||||
features['klc_pullback'] = 0
|
||||
|
||||
# ===== 3.8 复杂形态识别因子 =====
|
||||
|
||||
# 双顶/双底形态
|
||||
if self.pre and self.pre.pre and self.pre.pre.pre and self.pre.pre.pre.pre:
|
||||
p5 = self.pre.pre.pre.pre
|
||||
p4 = self.pre.pre.pre
|
||||
p3 = self.pre.pre
|
||||
p2 = self.pre
|
||||
p1 = self
|
||||
|
||||
# 双顶检测 (M形)
|
||||
double_top = (p5.high < p4.high and p4.high > p3.high and
|
||||
p3.high < p2.high and p2.high > p1.high and
|
||||
abs(p4.high - p2.high) / p4.high < 0.03) # 两个顶的高度接近
|
||||
|
||||
# 双底检测 (W形)
|
||||
double_bottom = (p5.low > p4.low and p4.low < p3.low and
|
||||
p3.low > p2.low and p2.low < p1.low and
|
||||
abs(p4.low - p2.low) / p4.low < 0.03) # 两个底的低点接近
|
||||
|
||||
features['klc_double_top'] = 1 if double_top else 0
|
||||
features['klc_double_bottom'] = 1 if double_bottom else 0
|
||||
else:
|
||||
features['klc_double_top'] = 0
|
||||
features['klc_double_bottom'] = 0
|
||||
|
||||
# 头肩顶/底形态
|
||||
if self.pre and self.pre.pre and self.pre.pre.pre and self.pre.pre.pre.pre and self.pre.pre.pre.pre.pre:
|
||||
p7 = self.pre.pre.pre.pre.pre
|
||||
p6 = self.pre.pre.pre.pre
|
||||
p5 = self.pre.pre.pre
|
||||
p4 = self.pre.pre
|
||||
p3 = self.pre
|
||||
p2 = self
|
||||
|
||||
# 头肩顶 (左肩-头-右肩)
|
||||
head_shoulders_top = (p7.high < p6.high and p6.high > p5.high and
|
||||
p5.high < p4.high and p4.high > p3.high and
|
||||
p3.high < p2.high and
|
||||
abs(p6.high - p2.high) / p6.high < 0.05 and # 左肩和右肩高度接近
|
||||
p4.high > p6.high and p4.high > p2.high) # 头部高于肩部
|
||||
|
||||
# 头肩底 (左肩-头-右肩)
|
||||
head_shoulders_bottom = (p7.low > p6.low and p6.low < p5.low and
|
||||
p5.low > p4.low and p4.low < p3.low and
|
||||
p3.low > p2.low and
|
||||
abs(p6.low - p2.low) / p6.low < 0.05 and # 左肩和右肩低点接近
|
||||
p4.low < p6.low and p4.low < p2.low) # 头部低于肩部
|
||||
|
||||
features['klc_head_shoulders_top'] = 1 if head_shoulders_top else 0
|
||||
features['klc_head_shoulders_bottom'] = 1 if head_shoulders_bottom else 0
|
||||
else:
|
||||
features['klc_head_shoulders_top'] = 0
|
||||
features['klc_head_shoulders_bottom'] = 0
|
||||
|
||||
# 旗形/三角形
|
||||
if prev_klcs and len(prev_klcs) >= 5:
|
||||
import numpy as np
|
||||
|
||||
highs = [self.high] + [klc.high for klc in prev_klcs[:5]]
|
||||
lows = [self.low] + [klc.low for klc in prev_klcs[:5]]
|
||||
|
||||
# 计算高点趋势线斜率
|
||||
x = np.arange(len(highs))
|
||||
high_slope, _ = np.polyfit(x, highs, 1)
|
||||
|
||||
# 计算低点趋势线斜率
|
||||
low_slope, _ = np.polyfit(x, lows, 1)
|
||||
|
||||
# 旗形: 高点和低点趋势线平行且方向相同
|
||||
if abs(high_slope - low_slope) / (abs(high_slope) + 1e-10) < 0.2:
|
||||
features['klc_flag_pattern'] = 1
|
||||
else:
|
||||
features['klc_flag_pattern'] = 0
|
||||
|
||||
# 上升三角形: 高点趋势线水平，低点趋势线向上
|
||||
if abs(high_slope) < 0.01 and low_slope > 0.01:
|
||||
features['klc_ascending_triangle'] = 1
|
||||
else:
|
||||
features['klc_ascending_triangle'] = 0
|
||||
|
||||
# 下降三角形: 高点趋势线向下，低点趋势线水平
|
||||
if high_slope < -0.01 and abs(low_slope) < 0.01:
|
||||
features['klc_descending_triangle'] = 1
|
||||
else:
|
||||
features['klc_descending_triangle'] = 0
|
||||
|
||||
# 对称三角形: 高点趋势线向下，低点趋势线向上
|
||||
if high_slope < -0.01 and low_slope > 0.01:
|
||||
features['klc_symmetric_triangle'] = 1
|
||||
else:
|
||||
features['klc_symmetric_triangle'] = 0
|
||||
else:
|
||||
features['klc_flag_pattern'] = 0
|
||||
features['klc_ascending_triangle'] = 0
|
||||
features['klc_descending_triangle'] = 0
|
||||
features['klc_symmetric_triangle'] = 0
|
||||
|
||||
# ===== 3.9 微观结构因子 =====
|
||||
|
||||
# 价格动量加速度
|
||||
if self.pre and self.pre.pre and self.pre.pre.pre:
|
||||
mom1 = self.close - self.pre.close
|
||||
mom2 = self.pre.close - self.pre.pre.close
|
||||
mom3 = self.pre.pre.close - self.pre.pre.pre.close
|
||||
|
||||
# 一阶动量变化
|
||||
features['klc_mom_change_1'] = mom1 - mom2
|
||||
|
||||
# 二阶动量变化
|
||||
features['klc_mom_change_2'] = (mom1 - mom2) - (mom2 - mom3)
|
||||
|
||||
# 动量方向变化
|
||||
features['klc_mom_direction_change'] = 1 if (mom1 > 0 and mom2 < 0) or (mom1 < 0 and mom2 > 0) else 0
|
||||
else:
|
||||
features['klc_mom_change_1'] = 0
|
||||
features['klc_mom_change_2'] = 0
|
||||
features['klc_mom_direction_change'] = 0
|
||||
|
||||
# 微观价格结构分析
|
||||
if self.pre:
|
||||
# K线重叠程度
|
||||
overlap_range = min(self.high, self.pre.high) - max(self.low, self.pre.low)
|
||||
total_range = max(self.high, self.pre.high) - min(self.low, self.pre.low)
|
||||
|
||||
if total_range > 0:
|
||||
features['klc_overlap_ratio'] = max(0, overlap_range) / total_range
|
||||
else:
|
||||
features['klc_overlap_ratio'] = 0
|
||||
|
||||
# 收盘价在当前K线的相对位置
|
||||
if self.high > self.low:
|
||||
features['klc_close_position_inbar'] = (self.close - self.low) / (self.high - self.low)
|
||||
else:
|
||||
features['klc_close_position_inbar'] = 0.5
|
||||
|
||||
# 当前K线相对于前一根K线的位置
|
||||
if self.pre.high > self.pre.low:
|
||||
features['klc_rel_position_to_prev'] = (self.close - self.pre.low) / (self.pre.high - self.pre.low)
|
||||
else:
|
||||
features['klc_rel_position_to_prev'] = 0.5
|
||||
else:
|
||||
features['klc_overlap_ratio'] = 0
|
||||
features['klc_close_position_inbar'] = 0.5
|
||||
features['klc_rel_position_to_prev'] = 0.5
|
||||
|
||||
# 价格变化率序列
|
||||
if prev_klcs and len(prev_klcs) >= 3:
|
||||
ret1 = self.close / prev_klcs[0].close - 1 if prev_klcs[0].close > 0 else 0
|
||||
ret2 = prev_klcs[0].close / prev_klcs[1].close - 1 if prev_klcs[1].close > 0 else 0
|
||||
ret3 = prev_klcs[1].close / prev_klcs[2].close - 1 if prev_klcs[2].close > 0 else 0
|
||||
|
||||
features['klc_return_1'] = ret1
|
||||
features['klc_return_2'] = ret2
|
||||
features['klc_return_3'] = ret3
|
||||
|
||||
# 收益率加速度
|
||||
features['klc_return_accel_1'] = ret1 - ret2
|
||||
features['klc_return_accel_2'] = (ret1 - ret2) - (ret2 - ret3)
|
||||
else:
|
||||
features['klc_return_1'] = 0
|
||||
features['klc_return_2'] = 0
|
||||
features['klc_return_3'] = 0
|
||||
features['klc_return_accel_1'] = 0
|
||||
features['klc_return_accel_2'] = 0
|
||||
|
||||
# ===== 3.10 综合形态因子 =====
|
||||
|
||||
# 能量比率 (K线实体与影线比例)
|
||||
body_size = abs(self.close - self.open)
|
||||
if self.high > self.low:
|
||||
upper_shadow = self.high - max(self.open, self.close)
|
||||
lower_shadow = min(self.open, self.close) - self.low
|
||||
|
||||
features['klc_upper_shadow_ratio'] = upper_shadow / (self.high - self.low)
|
||||
features['klc_lower_shadow_ratio'] = lower_shadow / (self.high - self.low)
|
||||
features['klc_body_to_range_ratio'] = body_size / (self.high - self.low)
|
||||
else:
|
||||
features['klc_upper_shadow_ratio'] = 0
|
||||
features['klc_lower_shadow_ratio'] = 0
|
||||
features['klc_body_to_range_ratio'] = 1
|
||||
|
||||
# K线平衡点
|
||||
features['klc_balance_point'] = (self.high + self.low + self.close) / 3
|
||||
|
||||
# 与平衡点的距离
|
||||
if features['klc_balance_point'] > 0:
|
||||
features['klc_distance_to_balance'] = (self.close - features['klc_balance_point']) / features['klc_balance_point']
|
||||
else:
|
||||
features['klc_distance_to_balance'] = 0
|
||||
|
||||
# 波动性和趋势组合因子
|
||||
if 'klc_volatility' in features and 'klc_trend_slope_norm' in features:
|
||||
features['klc_volatility_trend_ratio'] = features['klc_volatility'] / (abs(features['klc_trend_slope_norm']) + 1e-10)
|
||||
else:
|
||||
features['klc_volatility_trend_ratio'] = 0
|
||||
|
||||
# K线逆转形态
|
||||
if self.pre:
|
||||
# 看涨逆转 (前一根阴线，当前阳线，且当前收盘高于前一根中点)
|
||||
bullish_reversal = (self.pre.close < self.pre.open and # 前一根阴线
|
||||
self.close > self.open and # 当前阳线
|
||||
self.close > (self.pre.high + self.pre.low) / 2) # 收盘价高于前一根中点
|
||||
|
||||
# 看跌逆转 (前一根阳线，当前阴线，且当前收盘低于前一根中点)
|
||||
bearish_reversal = (self.pre.close > self.pre.open and # 前一根阳线
|
||||
self.close < self.open and # 当前阴线
|
||||
self.close < (self.pre.high + self.pre.low) / 2) # 收盘价低于前一根中点
|
||||
|
||||
features['klc_bullish_reversal'] = 1 if bullish_reversal else 0
|
||||
features['klc_bearish_reversal'] = 1 if bearish_reversal else 0
|
||||
else:
|
||||
features['klc_bullish_reversal'] = 0
|
||||
features['klc_bearish_reversal'] = 0
|
||||
|
||||
# 特殊K线形态
|
||||
# 大阳线/大阴线
|
||||
avg_body = 0
|
||||
if prev_klcs and len(prev_klcs) >= 5:
|
||||
bodies = [abs(klc.close - klc.open) for klc in prev_klcs[:5]]
|
||||
avg_body = sum(bodies) / len(bodies) if bodies else 0
|
||||
|
||||
if avg_body > 0:
|
||||
features['klc_large_candle'] = body_size / avg_body
|
||||
else:
|
||||
features['klc_large_candle'] = 1
|
||||
|
||||
# 长上影线/长下影线
|
||||
if self.high > self.low:
|
||||
upper_shadow_ratio = (self.high - max(self.open, self.close)) / (self.high - self.low)
|
||||
lower_shadow_ratio = (min(self.open, self.close) - self.low) / (self.high - self.low)
|
||||
|
||||
features['klc_long_upper_shadow'] = 1 if upper_shadow_ratio > 0.6 else 0
|
||||
features['klc_long_lower_shadow'] = 1 if lower_shadow_ratio > 0.6 else 0
|
||||
else:
|
||||
features['klc_long_upper_shadow'] = 0
|
||||
features['klc_long_lower_shadow'] = 0
|
||||
|
||||
# 星线形态 (当前K线实体小，且与前一根K线有缺口)
|
||||
if self.pre and (self.high - self.low) > 0:
|
||||
small_body = body_size / (self.high - self.low) < 0.3
|
||||
gap_with_prev = (min(self.open, self.close) > self.pre.close) if self.pre.close > self.pre.open else (max(self.open, self.close) < self.pre.close)
|
||||
|
||||
features['klc_star_pattern'] = 1 if small_body and gap_with_prev else 0
|
||||
else:
|
||||
features['klc_star_pattern'] = 0
|
||||
|
||||
return features
|
||||
Reference in New Issue
Block a user