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Add classifier to the code

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jackyu66git
2025-04-24 20:34:16 +08:00
parent 656484e0ca
commit 63852efe47
7 changed files with 42 additions and 50 deletions
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ChanLun_Classifier.py
+29 -37
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@@ -6,7 +6,7 @@ sys.path.append(os.path.abspath("/Users/jack/Project/freqtrade/user_data/Chan"))
import numpy as np
from datetime import timedelta
from pandas import DataFrame
from ChanEnum import Chan_FX_TYPE, Chan_KLINE_DIR, Chan_BI_DIR, Chan_SEG_DIR, Chan_ZS_DIR, Chan_BSP_DIR, Chan_BSP_TYPE
from ChanEnum import Chan_FX_TYPE, Chan_KLINE_DIR, Chan_BI_DIR, Chan_SEG_DIR, Chan_ZS_DIR, Chan_BSP_DIR, Chan_BSP_TYPE, Chan_KLC_FX
from ChanKLU import ChanKLU
from ChanKLC import ChanKLC
from ChanBI import ChanBI
@@ -279,31 +279,24 @@ class ChanLunClassifier:
:return: 特征矩阵X和标签y
"""
# 使用ChanLun获取bi_list
bi_list = self.chan.cal_bi_list(self.chan.get_klc_list(dataframe))
klc_list = self.chan.get_klc_list(dataframe)
bi_list = self.chan.cal_bi_list(klc_list)
seg_list = self.chan.get_seg_list(bi_list)
# 筛选方向为UP的bi的起始klc
feature_data = []
labels = []
bi_index = 0
bi_index = 1
sample_list = []
for klc in klc_list:
if klc.pre and klc.next:
if klc.high > klc.pre.high and klc.high > klc.next.high:
klc.set_fx(Chan_FX_TYPE.TOP)
elif klc.low < klc.pre.low and klc.low < klc.next.low:
klc.set_fx(Chan_FX_TYPE.BOTTOM)
else:
klc.set_fx(Chan_FX_TYPE.UNKNOWN)
if klc.fx != Chan_FX_TYPE.UNKNOWN:
if klc.klc_fx_type != Chan_KLC_FX.UNKNOWN:
sample_list.append(klc)
for klc in sample_list:
if bi_index >= len(bi_list):
bi_index = len(bi_list) - 1
bi = bi_list[bi_index]
if klc.end_klu and bi.end_klc and klc.start_klu.index >= bi.start_klc.start_klu.index and klc.end_klu.index <= bi.end_klc.end_klu.index:
klc.set_bi(bi)
#bi = bi_list[bi_index]
#if klc.end_klu and bi.end_klc and klc.start_klu.index >= bi.start_klc.start_klu.index and klc.end_klu.index <= bi.end_klc.end_klu.index:
#klc.set_bi(bi)
# 提取特征
features = klc.get_feature_data()
@@ -318,17 +311,21 @@ class ChanLunClassifier:
# 判断这个bi是否赚钱（这里简单定义为：如果bi的结束价格高于起始价格，则标记为1，否则为0）
# 这个标签定义可以根据实际需求修改
bi = bi_list[bi_index]
if bi.start_klc.index == klc.index:
#if klc.index == seg.start_bi.start_klc.index and seg.dir == Chan_SEG_DIR.UP:
label = 1
bi_index += 2
else:
matched = False
for bi in bi_list:
if bi.end_klc and bi.end_klc.index == klc.index:
label = 1
matched = True
break
if not matched:
label = 0
feature_data.append(feature_vec)
labels.append(label)
print("Trainning data: ", klc_list[-1].start_time, klc_list[-1].fx)
# 在return前添加
positive_count = np.sum(labels)
print(f"正样本数量: {positive_count}, 负样本数量: {len(labels) - positive_count}")
print("Trainning data: ", len(feature_data), klc_list[-1].start_time, klc_list[-1].klc_fx_type , "---------------------")
return np.array(feature_data), np.array(labels)
def get_validate_feature_data(self, dataframe):
"""
@@ -337,23 +334,16 @@ class ChanLunClassifier:
:return: 特征矩阵X和标签y
"""
# 使用ChanLun获取bi_list
bi_list = self.chan.cal_bi_list(self.chan.get_klc_list(dataframe))
klc_list = self.chan.get_klc_list(dataframe)
bi_list = self.chan.cal_bi_list(klc_list)
seg_list = self.chan.get_seg_list(bi_list)
# 筛选方向为UP的bi的起始klc
feature_data = []
labels = []
bi_index = 0
bi_index = 1
sample_list = []
for klc in klc_list:
if klc.pre and klc.next:
if klc.high > klc.pre.high and klc.high > klc.next.high:
klc.set_fx(Chan_FX_TYPE.TOP)
elif klc.low < klc.pre.low and klc.low < klc.next.low:
klc.set_fx(Chan_FX_TYPE.BOTTOM)
else:
klc.set_fx(Chan_FX_TYPE.UNKNOWN)
if klc.fx != Chan_FX_TYPE.UNKNOWN:
if klc.klc_fx_type != Chan_KLC_FX.UNKNOWN:
sample_list.append(klc)
for klc in sample_list:
if bi_index >= len(bi_list):
@@ -371,16 +361,18 @@ class ChanLunClassifier:
else:
feature_vec.append(0)
seg = seg_list[bi_index]
if bi.start_klc.index == klc.index:
#if klc.index == seg.start_bi.start_klc.index and seg.dir == Chan_SEG_DIR.UP:
label = 1
bi_index += 2
else:
matched = False
for bi in bi_list:
if bi.end_klc and bi.end_klc.index == klc.index:
label = 1
matched = True
break
if not matched:
label = 0
feature_data.append(feature_vec)
labels.append(label)
print("Validating data: ", len(feature_data), klc_list[-1].start_time, klc_list[-1].klc_fx_type , "---------------------")
return np.array(feature_data), np.array(labels)
def validate_model(self, dataframe=None):
"""