Add some classifier code
This commit is contained in:
jackyu66git
+82
-31
@@ -15,6 +15,9 @@ from matplotlib.dates import DateFormatter, date2num
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import matplotlib.patches as patches
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from technical.util import resample_to_interval
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from decimal import Decimal
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import xgboost as xgb
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import numpy as np
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class ChanLun():
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timeframes = ["5m", "15m", "30m", "60m", "4h"]
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times = {
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@@ -29,7 +32,6 @@ class ChanLun():
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time30 = 30
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time60 = 60
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time4h = 240
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def create_all_data(self, dataframe, ticker_indicator):
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all_data = dict()
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all_data['1m'] = dataframe
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@@ -910,8 +912,8 @@ class ChanLun():
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#self.print_zs(zs_list)
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return zs_list
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def get_bi_macd_hist_list(self, bi_list, dataframe):
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bi_macd_hist_list = []
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def get_bi_macdhist_list(self, bi_list, dataframe):
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bi_macdhist_list = []
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for bi in bi_list:
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start_index = bi.start_klc.start_klu.index
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if bi.end_klc:
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@@ -925,12 +927,12 @@ class ChanLun():
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total_macd_hist += macd_hist
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if bi.dir == Chan_BI_DIR.DOWN and macd_hist < 0:
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total_macd_hist -= macd_hist
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bi_macd_hist_list.append(abs(total_macd_hist))
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bi.set_macd_hist(total_macd_hist)
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return bi_macd_hist_list, bi_list
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bi_macdhist_list.append(abs(total_macd_hist))
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bi.set_macdhist(total_macd_hist)
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return bi_macdhist_list, bi_list
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def get_seg_macd_hist_list(self, seg_list, dataframe):
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seg_macd_hist_list = []
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def get_seg_macdhist_list(self, seg_list, dataframe):
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seg_macdhist_list = []
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for seg in seg_list:
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start_index = seg.start_bi.start_klc.start_klu.index
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if seg.end_bi:
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@@ -944,46 +946,46 @@ class ChanLun():
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total_macd_hist += macd_hist
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if seg.dir == Chan_SEG_DIR.DOWN and macd_hist < 0:
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total_macd_hist -= macd_hist
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seg_macd_hist_list.append(abs(total_macd_hist))
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seg.set_macd_hist(total_macd_hist)
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return seg_macd_hist_list, seg_list
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seg_macdhist_list.append(abs(total_macd_hist))
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seg.set_macdhist(total_macd_hist)
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return seg_macdhist_list, seg_list
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def get_bi_macd_div_list(self, bi_list, dataframe):
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bi_macd_div_list = []
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bi_macd_hist_list, bi_list = self.get_bi_macd_hist_list(bi_list, dataframe)
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bi_macdhist_list, bi_list = self.get_bi_macdhist_list(bi_list, dataframe)
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for index in range(2, len(bi_list)):
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if bi_macd_hist_list[index-2] == 0:
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if bi_macdhist_list[index-2] == 0:
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bi_macd_div = 0.0
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if index > 3 and bi_macd_hist_list[index-4] > 0.0:
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bi_macd_div = bi_macd_hist_list[index]/bi_macd_hist_list[index-4]
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if index > 3 and bi_macdhist_list[index-4] > 0.0:
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bi_macd_div = bi_macdhist_list[index]/bi_macdhist_list[index-4]
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else:
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bi_macd_div = bi_macd_hist_list[index]/bi_macd_hist_list[index-2]
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bi_macd_div = bi_macdhist_list[index]/bi_macdhist_list[index-2]
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if bi_macd_div < 0.01:
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if index > 3 and bi_macd_hist_list[index-4] > 0.0:
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bi_macd_div = bi_macd_hist_list[index]/bi_macd_hist_list[index-4]
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if index > 3 and bi_macdhist_list[index-4] > 0.0:
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bi_macd_div = bi_macdhist_list[index]/bi_macdhist_list[index-4]
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bi_macd_div = self.get_decimal(bi_macd_div)
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bi_macd_div_list.append(bi_macd_div)
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bi_list[index].set_macd_div(bi_macd_div)
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#print(bi_list[index].start_klc.start_time, self.get_decimal(bi_macd_hist_list[index]), self.get_decimal(bi_macd_hist_list[index - 1]), self.get_decimal(bi_macd_div))
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#print(bi_list[index].start_klc.start_time, self.get_decimal(bi_macdhist_list[index]), self.get_decimal(bi_macdhist_list[index - 1]), self.get_decimal(bi_macd_div))
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return bi_macd_div_list, bi_list
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def get_seg_macd_div_list(self, seg_list, dataframe):
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seg_macd_div_list = []
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seg_macd_hist_list, seg_list = self.get_seg_macd_hist_list(seg_list, dataframe)
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seg_macdhist_list, seg_list = self.get_seg_macdhist_list(seg_list, dataframe)
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for index in range(2, len(seg_list)):
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if seg_macd_hist_list[index-2] == 0:
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if seg_macdhist_list[index-2] == 0:
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seg_macd_div = 0.0
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if index > 3 and seg_macd_hist_list[index-4] > 0.0:
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seg_macd_div = seg_macd_hist_list[index]/seg_macd_hist_list[index - 4]
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if index > 3 and seg_macdhist_list[index-4] > 0.0:
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seg_macd_div = seg_macdhist_list[index]/seg_macdhist_list[index - 4]
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else:
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seg_macd_div = seg_macd_hist_list[index]/seg_macd_hist_list[index - 2]
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seg_macd_div = seg_macdhist_list[index]/seg_macdhist_list[index - 2]
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if seg_macd_div < 0.01:
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if index > 3 and seg_macd_hist_list[index-4] > 0.0:
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seg_macd_div = seg_macd_hist_list[index]/seg_macd_hist_list[index - 4]
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if index > 3 and seg_macdhist_list[index-4] > 0.0:
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seg_macd_div = seg_macdhist_list[index]/seg_macdhist_list[index - 4]
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seg_macd_div = self.get_decimal(seg_macd_div)
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seg_macd_div_list.append(seg_macd_div)
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seg_list[index].set_macd_div(seg_macd_div)
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#print(seg_list[index].start_bi.start_klc.start_time, self.get_decimal(seg_macd_hist_list[index]), self.get_decimal(seg_macd_hist_list[index - 1]), self.get_decimal(seg_macd_div))
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#print(seg_list[index].start_bi.start_klc.start_time, self.get_decimal(seg_macdhist_list[index]), self.get_decimal(seg_macdhist_list[index - 1]), self.get_decimal(seg_macd_div))
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return seg_macd_div_list, seg_list
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def get_macd_div_list(self, dataframe):
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@@ -1596,7 +1598,7 @@ class ChanLun():
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for index in range(0, len(big_bi_macd_div)):
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bi_macd_div = big_bi_macd_div[index]
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bi = big_bi[index + 2]
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if bi.end_klc:
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if bi.end_klc and False:
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text_index = bi.end_klc.end_klu.index
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if bi.dir == Chan_BI_DIR.UP:
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ax1.text(big_dates_num[text_index], bi.end_klc.high+1, bi_macd_div, color='red', fontsize=10, alpha=0.6)
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@@ -1605,12 +1607,24 @@ class ChanLun():
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for index in range(0, len(big_seg_macd_div)):
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seg_macd_div = big_seg_macd_div[index]
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seg = big_seg[index + 2]
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if seg.end_bi:
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if seg.end_bi and False:
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text_index = seg.end_bi.end_klc.end_klu.index
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if seg.dir == Chan_SEG_DIR.UP:
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ax1.text(big_dates_num[text_index], seg.end_bi.end_klc.high+1, seg_macd_div, color='red', fontsize=14, alpha=0.6)
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else:
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ax1.text(big_dates_num[text_index], seg.end_bi.end_klc.low-1, seg_macd_div, color='green', fontsize=14, alpha=0.6)
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big_klc = self.get_klc_list(big_df)
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model = xgb.Booster()
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model.load_model("30m_modelchan_xgb_model.json")
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for klc in big_klc:
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predict = self.predict(klc, model)
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if predict > 0.35 and klc.fx == Chan_FX_TYPE.BOTTOM:
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text_index = klc.end_klu.index
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ax1.text(big_dates_num[text_index], klc.high+1, predict, color='red', fontsize=14, alpha=0.6)
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if predict > 0.35 and klc.fx == Chan_FX_TYPE.TOP:
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text_index = klc.end_klu.index
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ax1.text(big_dates_num[text_index], klc.low-1, predict, color='green', fontsize=14, alpha=0.6)
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"""
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# 绘制大周期买卖点
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marker_styles = {
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@@ -1748,7 +1762,7 @@ class ChanLun():
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for index in range(0, len(small_bi_macd_div)):
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bi_macd_div = small_bi_macd_div[index]
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bi = small_bi[index + 2]
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if bi.end_klc:
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if bi.end_klc and False:
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text_index = bi.end_klc.end_klu.index
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if bi.dir == Chan_BI_DIR.UP:
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ax2.text(small_dates_num[text_index], bi.end_klc.high+1, bi_macd_div, color='red', fontsize=10, alpha=0.6)
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@@ -1757,12 +1771,23 @@ class ChanLun():
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for index in range(0, len(small_seg_macd_div)):
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seg_macd_div = small_seg_macd_div[index]
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seg = small_seg[index + 2]
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if seg.end_bi:
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if seg.end_bi and False:
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text_index = seg.end_bi.end_klc.end_klu.index
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if seg.dir == Chan_SEG_DIR.UP:
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ax2.text(small_dates_num[text_index], seg.end_bi.end_klc.high+1, seg_macd_div, color='red', fontsize=14, alpha=0.8)
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else:
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ax2.text(small_dates_num[text_index], seg.end_bi.end_klc.low-1, seg_macd_div, color='green', fontsize=14, alpha=0.8)
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small_klc = self.get_klc_list(small_df)
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model = xgb.Booster()
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model.load_model("5m_modelchan_xgb_model.json")
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for klc in small_klc:
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predict = self.predict(klc, model)
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if predict > 0.35 and klc.fx == Chan_FX_TYPE.BOTTOM:
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text_index = klc.end_klu.index
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ax2.text(small_dates_num[text_index], klc.high+1, predict, color='red', fontsize=14, alpha=0.6)
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if predict > 0.35 and klc.fx == Chan_FX_TYPE.TOP:
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text_index = klc.end_klu.index
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ax2.text(small_dates_num[text_index], klc.low-1, predict, color='green', fontsize=14, alpha=0.6)
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"""
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# 绘制小周期买卖点
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for idx, point in small_buy_sell_points.items():
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@@ -1806,7 +1831,33 @@ class ChanLun():
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plt.xticks(rotation=45)
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plt.tight_layout()
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plt.show()
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def predict(self, klc, model):
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"""
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使用训练好的模型预测单个KLC
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:param klc: 需要预测的ChanKLC对象
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:return: 预测结果（概率值）
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"""
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# 提取特征
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features = klc.get_feature_data()
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feature_vec = []
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# 与get_feature_data保持一致，只使用相同的特征集
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for key, value in features.items():
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if isinstance(value, (int, float)):
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feature_vec.append(value)
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else:
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feature_vec.append(0)
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# 转换为模型输入格式
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dtest = xgb.DMatrix(np.array([feature_vec]))
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# 预测
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return self.get_decimal(model.predict(dtest)[0])
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def get_decimal(self, value):
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return Decimal("{:.2f}".format(value))
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def plot(self, dataframe, bi_list, seg_list, zs_list=None, buy_sell_points=None, divergence_points=None):
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"""
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绘制缠论分析图表，包括K线、笔、线段、中枢、买卖点和MACD背驰
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