jack/chan.py
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2025-06-10 01:16:09 +08:00
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Debug/.DS_Store
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import json
from typing import Dict, TypedDict
import sys
import os
sys.path.append(os.path.abspath("/Users/jack/Project/chan.py"))
import xgboost as xgb
from BuySellPoint.BS_Point import CBS_Point
from Chan import CChan
from ChanConfig import CChanConfig
from ChanModel.Features import CFeatures
from Common.CEnum import AUTYPE, DATA_SRC, KL_TYPE, BSP_TYPE, MACD_ALGO
from Common.CTime import CTime
from Plot.PlotDriver import CPlotDriver
from Bi.Bi import CBi
class FeatureDict:
def klc_features(self, klc):
# klc features
klc_feature_dict = {
"klu_max_high": klc.get_klu_max_high(),
"klu_max_low": klc.get_klu_min_low(),
}
return klc_feature_dict
def bi_features(self, bi):
# bi features
bi_feature_dict = {
"bi_macd_area": bi.cal_macd_metric(MACD_ALGO.AREA, False),
"bi_macd_peak": bi.cal_macd_metric(MACD_ALGO.PEAK, False),
"bi_macd_full_area": bi.cal_macd_metric(MACD_ALGO.FULL_AREA, False),
"bi_macd_diff": bi.cal_macd_metric(MACD_ALGO.DIFF, False),
"bi_macd_slop": bi.cal_macd_metric(MACD_ALGO.SLOPE, False),
"bi_macd_amp": bi.cal_macd_metric(MACD_ALGO.AMP, False),
"bi_macd_amount": bi.cal_macd_metric(MACD_ALGO.AMOUNT, False),
"bi_macd_rsi": bi.cal_macd_metric(MACD_ALGO.RSI, False),
}
#bi_feature_dict.update(self.klc_features(bi.begin_klc))
#bi_feature_dict.update(self.klc_features(bi.end_klc))
return bi_feature_dict
def seg_features(self, seg):
seg_feature_dict = {
# seg features
"seg_macd_slope": seg.Cal_MACD_slope(),
"seg_macd_amp": seg.Cal_MACD_amp(),
"seg_amp": seg.amp(),
"seg_cal_klu_slope": seg.cal_klu_slope(),
"seg_cal_amp": seg.amp(),
}
return seg_feature_dict
def zs_features(self, zs):
zs_feature_dict = {
f"zs_high": zs.high,
f"zs_low": zs.low,
f"zs_mid": zs.mid,
f"zs_peak_low": zs.peak_low,
f"zs_peak_high": zs.peak_high,
}
#zs_feature_dict.update(bi_features(zs.begin_bi))
#zs_feature_dict.update(bi_features(zs.end_bi))
#zs_feature_dict.update(bi_features(zs.bi_in))
#zs_feature_dict.update(bi_features(zs.bi_out))
return zs_feature_dict
def klu_features(self, klu):
klu_feature_dict = {
# klu features
"klu_rsi": klu.rsi,
"klu_macd_fast_ema": klu.macd.fast_ema,
"klu_macd_slow_ema": klu.macd.slow_ema,
"klu_macd_DIF": klu.macd.DIF,
"klu_macd_DEA": klu.macd.DEA,
"klu_macd_macd": klu.macd.macd,
"klu_kdj_k": klu.kdj.k,
"klu_kdj_d": klu.kdj.d,
"klu_kdj_j": klu.kdj.j,
}
return klu_feature_dict
def stragety_feature(self, last_bsp):
last_bi = last_bsp.bi
last_klu = last_bsp.klu
feature_dict = {
"open_klu_rate": (last_klu.close - last_klu.open)/last_klu.open,
}
# add klu features
feature_dict.update(self.klu_features(last_klu))
# add bi features
#feature_dict.update(self.bi_features(last_bi))
# add seg features
seg = last_bi.parent_seg
#feature_dict.update(seg_features(seg))
# add zs features
zs_list = seg.zs_lst
zs_feature_dict = {}
if zs_list:
zs_feature_dict = {
f"zs_count": len(zs_list),
}
bi_in_zs = False
for zs in zs_list:
if last_bi in zs.bi_lst:
bi_in_zs = True
if bi_in_zs:
zs_feature_dict.update({
"bi_in_zs": 1
})
else:
zs_feature_dict.update({
"bi_in_zs": 0
})
zs_feature_dict.update(self.zs_features(zs_list[-1]))
else:
zs_feature_dict ={
"zs_count": 0,
}
feature_dict.update(zs_feature_dict)
return feature_dict
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Debug/buy_strategy.py
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import json
from typing import Dict, TypedDict
import sys
import os
sys.path.append(os.path.abspath("/Users/jack/Project/chan.py"))
sys.path.append(os.path.abspath("/Users/jack/Project/chan.py/Debug"))
import xgboost as xgb
from Chan import CChan
from ChanConfig import CChanConfig
from ChanModel.Features import CFeatures
from Common.CEnum import AUTYPE, DATA_SRC, KL_TYPE, BSP_TYPE, MACD_ALGO
from Common.CTime import CTime
from Plot.PlotDriver import CPlotDriver
from Bi.Bi import CBi
from FeatureDict import FeatureDict
class T_SAMPLE_INFO(TypedDict):
feature: CFeatures
is_buy: bool
open_time: CTime
def plot(chan, plot_marker):
plot_config = {
"plot_kline": True,
"plot_bi": True,
"plot_seg": True,
"plot_zs": True,
"plot_bsp": True,
"plot_marker": True,
"plot_macd": True,
}
plot_para = {
"figure": {
"x_range": 4000,
},
"marker": {
"markers": plot_marker
}
}
plot_driver = CPlotDriver(
chan,
plot_config=plot_config,
plot_para=plot_para,
)
plot_driver.save2img("buy.png")
if __name__ == "__main__":
"""
本demo主要演示如何记录策略产出的买卖点的特征
然后将这些特征作为样本，训练一个模型(以XGB为demo)
用于预测买卖点的准确性
请注意，demo训练预测都用的是同一份数据，这是不合理的，仅仅是为了演示
"""
code = "BTC/USDT:USDT"
begin_time = "2024-12-1"
end_time = "2024-12-4"
data_src = DATA_SRC.CCXT
lv_list = [KL_TYPE.K_1M]
config = CChanConfig({
"trigger_step": True, # 打开开关！
"bi_strict": True,
"skip_step": 0,
"divergence_rate": float("inf"),
"bsp2_follow_1": False,
"bsp3_follow_1": False,
"min_zs_cnt": 0,
"bs1_peak": False,
"macd_algo": "peak",
"bs_type": '1,2,3a,1p,2s,3b',
"print_warning": True,
"zs_algo": "normal",
"cal_rsi": True,
"cal_kdj": True,
})
chan = CChan(
code=code,
begin_time=begin_time,
end_time=end_time,
data_src=data_src,
lv_list=lv_list,
config=config,
autype=AUTYPE.QFQ,
)
bsp_dict: Dict[int, T_SAMPLE_INFO] = {} # 存储策略产出的bsp的特征
# 跑策略，保存买卖点的特征
index = 0
for chan_snapshot in chan.step_load():
last_klu = chan_snapshot[0][-1][-1]
bsp_list = chan_snapshot.get_bsp()
if not bsp_list:
continue
last_bsp = bsp_list[-1]
cur_lv_chan = chan_snapshot[0]
if BSP_TYPE.T1 in last_bsp.type or BSP_TYPE.T1P in last_bsp.type:
if last_bsp.klu.idx not in bsp_dict and cur_lv_chan[-2].idx == last_bsp.klu.klc.idx and last_bsp.is_buy:
# 假如策略是：买卖点分形第三元素出现时交易
bsp_dict[last_bsp.klu.idx] = {
"feature": last_bsp.features,
"is_buy": last_bsp.is_buy,
"open_time": last_klu.time,
}
bsp_dict[last_bsp.klu.idx]['feature'].add_feat(FeatureDict().stragety_feature(last_bsp)) # 开仓K线特征
print(index, last_bsp.klu.time, last_bsp.is_buy)
index += 1
# 生成libsvm样本特征
bsp_academy = [bsp.klu.idx for bsp in chan.get_bsp()]
feature_meta = {} # 特征meta
cur_feature_idx = 0
plot_marker = {}
fid = open("buy_feature.libsvm", "w")
for bsp_klu_idx, feature_info in bsp_dict.items():
label = int(bsp_klu_idx in bsp_academy) # 以买卖点识别是否准确为label
features = [] # List[(idx, value)]
for feature_name, value in feature_info['feature'].items():
if feature_name not in feature_meta:
feature_meta[feature_name] = cur_feature_idx
cur_feature_idx += 1
features.append((feature_meta[feature_name], value))
features.sort(key=lambda x: x[0])
feature_str = " ".join([f"{idx}:{value}" for idx, value in features])
fid.write(f"{label} {feature_str}\n")
plot_marker[feature_info["open_time"].to_str()] = ("√" if label else "×", "down" if feature_info["is_buy"] else "up")
fid.close()
with open("feature.meta", "w") as fid:
# meta保存下来，实盘预测时特征对齐用
fid.write(json.dumps(feature_meta))
# 画图检查label是否正确
plot(chan, plot_marker)
# load sample file & train model
dtrain = xgb.DMatrix("buy_feature.libsvm?format=libsvm") # load sample
param = {'max_depth': 2, 'eta': 0.3, 'objective': 'binary:logistic', 'eval_metric': 'auc'}
evals_result = {}
bst = xgb.train(
param,
dtrain=dtrain,
num_boost_round=50,
evals=[(dtrain, "train")],
evals_result=evals_result,
verbose_eval=True,
)
bst.save_model("buy_model.json")
# load model
model = xgb.Booster()
model.load_model("buy_model.json")
# predict
print(model.predict(dtrain))
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{"divergence_rate": 0, "open_klu_rate": 1, "klu_rsi": 2, "klu_macd_fast_ema": 3, "klu_macd_slow_ema": 4, "klu_macd_DIF": 5, "klu_macd_DEA": 6, "klu_macd_macd": 7, "klu_kdj_k": 8, "klu_kdj_d": 9, "klu_kdj_j": 10, "zs_count": 11, "bi_in_zs": 12, "zs_high": 13, "zs_low": 14, "zs_mid": 15, "zs_peak_low": 16, "zs_peak_high": 17}
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{"divergence_rate": 0, "open_klu_rate": 1, "klu_rsi": 2, "klu_macd_fast_ema": 3, "klu_macd_slow_ema": 4, "klu_macd_DIF": 5, "klu_macd_DEA": 6, "klu_macd_macd": 7, "klu_kdj_k": 8, "klu_kdj_d": 9, "klu_kdj_j": 10, "bi_macd_area": 11, "bi_macd_peak": 12, "bi_macd_full_area": 13, "bi_macd_diff": 14, "bi_macd_slop": 15, "bi_macd_amp": 16, "bi_macd_amount": 17, "bi_macd_rsi": 18, "klu_max_high": 19, "klu_max_low": 20, "seg_macd_slope": 21, "seg_macd_amp": 22, "seg_amp": 23, "seg_cal_klu_slope": 24, "seg_cal_amp": 25, "zs_count": 26, "bi_in_zs": 27, "zs_high": 28, "zs_low": 29, "zs_mid": 30, "zs_peak_low": 31, "zs_peak_high": 32}
Debug/models/buy_models/b1_models/20241211_1m/read_me.txt
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Read me
Training Data
Timerange: 20240101-20241130
Timeframe: 1m
Evaluation Data
Timerange: 20241205-20241207
Timeframe: 1m
Resulting Model Feature: 这个模型识别急跌情况下的b1，其它b1被忽略
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Debug/models/buy_models/b1_models/20241211_5m/read_me.txt
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Read me
Training Data
Timerange: 20220101-20241130
Timeframe: 5m
Evaluation Data
Timerange: 20241205-20241207
Timeframe: 5m
Resulting Model Feature:
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Debug/sell_model.json
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Debug/sell_strategy.py
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import json
from typing import Dict, TypedDict
import sys
import os
sys.path.append(os.path.abspath("/Users/jack/Project/chan.py"))
sys.path.append(os.path.abspath("/Users/jack/Project/chan.py/Debug"))
import xgboost as xgb
from Chan import CChan
from ChanConfig import CChanConfig
from ChanModel.Features import CFeatures
from Common.CEnum import AUTYPE, DATA_SRC, KL_TYPE, BSP_TYPE, MACD_ALGO
from Common.CTime import CTime
from Plot.PlotDriver import CPlotDriver
from Bi.Bi import CBi
from FeatureDict import FeatureDict
class T_SAMPLE_INFO(TypedDict):
feature: CFeatures
is_buy: bool
open_time: CTime
def plot(chan, plot_marker):
plot_config = {
"plot_kline": True,
"plot_bi": True,
"plot_seg": True,
"plot_zs": True,
"plot_bsp": True,
"plot_marker": True,
"plot_macd": True,
}
plot_para = {
"figure": {
"x_range": 4000,
},
"marker": {
"markers": plot_marker
}
}
plot_driver = CPlotDriver(
chan,
plot_config=plot_config,
plot_para=plot_para,
)
plot_driver.save2img("sell.png")
if __name__ == "__main__":
"""
本demo主要演示如何记录策略产出的买卖点的特征
然后将这些特征作为样本，训练一个模型(以XGB为demo)
用于预测买卖点的准确性
请注意，demo训练预测都用的是同一份数据，这是不合理的，仅仅是为了演示
"""
code = "BTC/USDT:USDT"
begin_time = "2024-12-1"
end_time = "2024-12-4"
data_src = DATA_SRC.CCXT
lv_list = [KL_TYPE.K_1M]
config = CChanConfig({
"trigger_step": True, # 打开开关！
"bi_strict": True,
"skip_step": 0,
"divergence_rate": float("inf"),
"bsp2_follow_1": False,
"bsp3_follow_1": False,
"min_zs_cnt": 0,
"bs1_peak": False,
"macd_algo": "peak",
"bs_type": '1,2,3a,1p,2s,3b',
"print_warning": True,
"zs_algo": "normal",
"cal_rsi": True,
"cal_kdj": True,
})
chan = CChan(
code=code,
begin_time=begin_time,
end_time=end_time,
data_src=data_src,
lv_list=lv_list,
config=config,
autype=AUTYPE.QFQ,
)
bsp_dict: Dict[int, T_SAMPLE_INFO] = {} # 存储策略产出的bsp的特征
# 跑策略，保存买卖点的特征
index = 0
for chan_snapshot in chan.step_load():
last_klu = chan_snapshot[0][-1][-1]
bsp_list = chan_snapshot.get_bsp()
if not bsp_list:
continue
last_bsp = bsp_list[-1]
cur_lv_chan = chan_snapshot[0]
if BSP_TYPE.T1 in last_bsp.type or BSP_TYPE.T1P in last_bsp.type:
if last_bsp.klu.idx not in bsp_dict and cur_lv_chan[-2].idx == last_bsp.klu.klc.idx and not last_bsp.is_buy:
# 假如策略是：买卖点分形第三元素出现时交易
bsp_dict[last_bsp.klu.idx] = {
"feature": last_bsp.features,
"is_buy": last_bsp.is_buy,
"open_time": last_klu.time,
}
bsp_dict[last_bsp.klu.idx]['feature'].add_feat(FeatureDict().stragety_feature(last_bsp)) # 开仓K线特征
print(index, last_bsp.klu.time, last_bsp.is_buy)
index += 1
# 生成libsvm样本特征
bsp_academy = [bsp.klu.idx for bsp in chan.get_bsp()]
feature_meta = {} # 特征meta
cur_feature_idx = 0
plot_marker = {}
fid = open("sell_feature.libsvm", "w")
for bsp_klu_idx, feature_info in bsp_dict.items():
label = int(bsp_klu_idx in bsp_academy) # 以买卖点识别是否准确为label
features = [] # List[(idx, value)]
for feature_name, value in feature_info['feature'].items():
if feature_name not in feature_meta:
feature_meta[feature_name] = cur_feature_idx
cur_feature_idx += 1
features.append((feature_meta[feature_name], value))
features.sort(key=lambda x: x[0])
feature_str = " ".join([f"{idx}:{value}" for idx, value in features])
fid.write(f"{label} {feature_str}\n")
plot_marker[feature_info["open_time"].to_str()] = ("√" if label else "×", "down" if feature_info["is_buy"] else "up")
fid.close()
with open("feature.meta", "w") as fid:
# meta保存下来，实盘预测时特征对齐用
fid.write(json.dumps(feature_meta))
# 画图检查label是否正确
plot(chan, plot_marker)
# load sample file & train model
dtrain = xgb.DMatrix("sell_feature.libsvm?format=libsvm") # load sample
param = {'max_depth': 2, 'eta': 0.3, 'objective': 'binary:logistic', 'eval_metric': 'auc'}
evals_result = {}
bst = xgb.train(
param,
dtrain=dtrain,
num_boost_round=50,
evals=[(dtrain, "train")],
evals_result=evals_result,
verbose_eval=True,
)
bst.save_model("sell_model.json")
# load model
model = xgb.Booster()
model.load_model("sell_model.json")
# predict
print(model.predict(dtrain))
Debug/strategy_demo.py
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from Chan import CChan
from ChanConfig import CChanConfig
from Common.CEnum import AUTYPE, BSP_TYPE, DATA_SRC, FX_TYPE, KL_TYPE
if __name__ == "__main__":
"""
一个极其弱智的策略，只交易一类买卖点，底分型形成后就开仓，直到一类卖点顶分型形成后平仓
只用做展示如何自己实现策略，做回测用~
"""
code = "sz.000001"
begin_time = "2021-01-01"
end_time = None
data_src = DATA_SRC.BAO_STOCK
lv_list = [KL_TYPE.K_DAY]
config = CChanConfig({
"trigger_step": True, # 打开开关！
"divergence_rate": 0.8,
"min_zs_cnt": 1,
})
chan = CChan(
code=code,
begin_time=begin_time,
end_time=end_time,
data_src=data_src,
lv_list=lv_list,
config=config,
autype=AUTYPE.QFQ,
)
is_hold = False
last_buy_price = None
for chan_snapshot in chan.step_load(): # 每增加一根K线，返回当前静态精算结果
bsp_list = chan_snapshot.get_bsp() # 获取买卖点列表
if not bsp_list: # 为空
continue
last_bsp = bsp_list[-1] # 最后一个买卖点
if BSP_TYPE.T1 not in last_bsp.type and BSP_TYPE.T1P not in last_bsp.type: # 假如只做1类买卖点
continue
cur_lv_chan = chan_snapshot[0]
if last_bsp.klu.klc.idx != cur_lv_chan[-2].idx:
continue
if cur_lv_chan[-2].fx == FX_TYPE.BOTTOM and last_bsp.is_buy and not is_hold: # 底分型形成后开仓
last_buy_price = cur_lv_chan[-1][-1].close # 开仓价格为最后一根K线close
print(f'{cur_lv_chan[-1][-1].time}:buy price = {last_buy_price}')
is_hold = True
elif cur_lv_chan[-2].fx == FX_TYPE.TOP and not last_bsp.is_buy and is_hold: # 顶分型形成后平仓
sell_price = cur_lv_chan[-1][-1].close
print(f'{cur_lv_chan[-1][-1].time}:sell price = {sell_price}, profit rate = {(sell_price-last_buy_price)/last_buy_price*100:.2f}%')
is_hold = False
Debug/strategy_demo2.py
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import sys
import os
sys.path.append(os.path.abspath("/Users/jack/Project/chan.py"))
sys.path.append(os.path.abspath("/Users/jack/Project/chan.py/Debug"))
from Chan import CChan
from ChanConfig import CChanConfig
from Common.CEnum import AUTYPE, BSP_TYPE, DATA_SRC, FX_TYPE, KL_TYPE
from DataAPI.ccxt import CCXT
if __name__ == "__main__":
"""
一个极其弱智的策略，只交易一类买卖点，底分型形成后就开仓，直到一类卖点顶分型形成后平仓
只用做展示如何自己实现策略，做回测用~
相比于strategy_demo.py，本代码演示如何从CChan外部喂K线来触发内部缠论计算
"""
code = "BTC/USDT"
begin_time = "2025-02-01"
end_time = None
data_src_type = DATA_SRC.CCXT
lv_list = [KL_TYPE.K_60M]
config = CChanConfig({
"trigger_step": True,
"divergence_rate": 0.8,
"min_zs_cnt": 0,
})
chan = CChan(
code=code,
begin_time=begin_time, # 已经没啥用了这一行
end_time=end_time, # 已经没啥用了这一行
data_src=data_src_type, # 已经没啥用了这一行
lv_list=lv_list,
config=config,
autype=AUTYPE.QFQ, # 已经没啥用了这一行
)
CCXT.do_init()
data_src = CCXT(code, k_type=KL_TYPE.K_60M, begin_date=begin_time, end_date=end_time, autype=AUTYPE.QFQ) # 初始化数据源类
is_hold = False
last_buy_price = None
for klu in data_src.get_kl_data(): # 获取单根K线
chan.trigger_load({KL_TYPE.K_60M: [klu]}) # 喂给CChan新增k线
bsp_list = chan.get_bsp()
if not bsp_list:
continue
last_bsp = bsp_list[-1]
print(klu.time, last_bsp.is_buy)
if BSP_TYPE.T1 not in last_bsp.type and BSP_TYPE.T1P not in last_bsp.type:
continue
cur_lv_chan = chan[0]
if last_bsp.klu.klc.idx != cur_lv_chan[-2].idx:
continue
if cur_lv_chan[-2].fx == FX_TYPE.BOTTOM and last_bsp.is_buy and not is_hold:
last_buy_price = cur_lv_chan[-1][-1].close
print(f'{cur_lv_chan[-1][-1].time}:buy price = {last_buy_price}')
is_hold = True
elif cur_lv_chan[-2].fx == FX_TYPE.TOP and not last_bsp.is_buy and is_hold:
sell_price = cur_lv_chan[-1][-1].close
print(f'{cur_lv_chan[-1][-1].time}:sell price = {sell_price}, profit rate = {(sell_price-last_buy_price)/last_buy_price*100:.2f}%')
is_hold = False
CCXT.do_close()
Debug/strategy_demo3.py
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import copy
from typing import List
from Chan import CChan
from ChanConfig import CChanConfig
from Common.CEnum import AUTYPE, DATA_FIELD, DATA_SRC, KL_TYPE
from DataAPI.BaoStockAPI import CBaoStock
from KLine.KLine_Unit import CKLine_Unit
def combine_60m_klu_form_15m(klu_15m_lst: List[CKLine_Unit]) -> CKLine_Unit:
return CKLine_Unit(
{
DATA_FIELD.FIELD_TIME: klu_15m_lst[-1].time,
DATA_FIELD.FIELD_OPEN: klu_15m_lst[0].open,
DATA_FIELD.FIELD_CLOSE: klu_15m_lst[-1].close,
DATA_FIELD.FIELD_HIGH: max(klu.high for klu in klu_15m_lst),
DATA_FIELD.FIELD_LOW: min(klu.low for klu in klu_15m_lst),
}
)
if __name__ == "__main__":
"""
代码不能直接跑，仅用于展示如何实现小级别K线更新直接刷新CChan结果
"""
code = "sz.000001"
begin_time = "2023-09-10"
end_time = None
data_src_type = DATA_SRC.BAO_STOCK
lv_list = [KL_TYPE.K_60M, KL_TYPE.K_15M]
config = CChanConfig({
"trigger_step": True,
})
# 快照
chan_snapshot = CChan(
code=code,
data_src=data_src_type,
lv_list=lv_list,
config=config,
)
CBaoStock.do_init()
data_src = CBaoStock(code, k_type=KL_TYPE.K_15M, begin_date=begin_time, end_date=end_time, autype=AUTYPE.QFQ) # 获取最小级别
klu_15m_lst_tmp: List[CKLine_Unit] = [] # 存储用于合成当前60M K线的15M k线
for klu_15m in data_src.get_kl_data(): # 获取单根15分钟K线
klu_15m_lst_tmp.append(klu_15m)
klu_60m = combine_60m_klu_form_15m(klu_15m_lst_tmp) # 合成60分钟K线
"""
拷贝一份chan_snapshot
如果是用序列化方式，这里可以采用pickle.load()
"""
chan: CChan = copy.deepcopy(chan_snapshot)
chan.trigger_load({KL_TYPE.K_60M: [klu_60m], KL_TYPE.K_15M: klu_15m_lst_tmp})
"""
策略开始：
这里基于chan实现你的策略
"""
for kl_type, ele_manager in chan.kl_datas.items():
# 打印当前每一级别分别有多少K线
print(klu_15m.time, kl_type, sum(len(klc) for klc in ele_manager))
# 策略结束：
if len(klu_15m_lst_tmp) == 4: # 已经完成4根15分钟K线了，说明这个最新的60分钟K线和里面的4根15分钟K线在将来不会再变化
"""
把当前完整chan重新保存成chan_snapshot
如果是序列化方式，这里可以采用pickle.dump()
"""
chan_snapshot = chan
klu_15m_lst_tmp = [] # 清空1分钟K线，用于下一个五分钟周期的合成
CBaoStock.do_close()
Debug/strategy_demo4.py
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from Chan import CChan
from ChanConfig import CChanConfig
from Common.CEnum import AUTYPE, DATA_SRC, KL_TYPE
from DataAPI.BaoStockAPI import CBaoStock
if __name__ == "__main__":
"""
本demo演示当你要用多级别trigger load的时候，如何巧妙的解决时间对齐的问题：
解决方案就是喂第一根最大级别的时候，直接把所有的次级别全部喂进去
之后每次只需要喂一根最大级别即可，复用框架内置的K线时间对齐能力
从而避免了在trigger_load入参那里进行K线对齐，自己去找出最大级别那根K线下所有次级别的K线
"""
code = "sz.000001"
begin_time = "2023-06-01"
end_time = None
data_src = DATA_SRC.BAO_STOCK
lv_list = [KL_TYPE.K_DAY, KL_TYPE.K_30M]
config = CChanConfig({
"trigger_step": True,
"divergence_rate": 0.8,
"min_zs_cnt": 1,
})
chan = CChan(
code=code,
begin_time=begin_time, # 已经没啥用了这一行
end_time=end_time, # 已经没啥用了这一行
data_src=data_src, # 已经没啥用了这一行
lv_list=lv_list,
config=config,
autype=AUTYPE.QFQ, # 已经没啥用了这一行
)
CBaoStock.do_init()
data_src_day = CBaoStock(code, k_type=KL_TYPE.K_DAY, begin_date=begin_time, end_date=end_time, autype=AUTYPE.QFQ)
data_src_30m = CBaoStock(code, k_type=KL_TYPE.K_30M, begin_date=begin_time, end_date=end_time, autype=AUTYPE.QFQ)
kl_30m_all = list(data_src_30m.get_kl_data())
for _idx, klu in enumerate(data_src_day.get_kl_data()):
# 本质是每喂一根日线的时候，这根日线之前的都要喂过，提前喂多点不要紧，框架会自动根据日线来截取需要的30M K线
# 30M一口气全部喂完，后续就不用关注时间对齐的问题了
if _idx == 0:
chan.trigger_load({KL_TYPE.K_DAY: [klu], KL_TYPE.K_30M: kl_30m_all})
else:
chan.trigger_load({KL_TYPE.K_DAY: [klu]})
if _idx == 4: # demo只检查4根日线
break
# 检查时间对齐
print("当前所有日线:", [klu.time.to_str() for klu in chan[0].klu_iter()])
print("当前所有30M K线:", [klu.time.to_str() for klu in chan[1].klu_iter()], "\n")
CBaoStock.do_close()
Debug/strategy_demo5.py
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import json
from typing import Dict, TypedDict
import sys
import os
sys.path.append(os.path.abspath("/Users/jack/Project/chan.py"))
import xgboost as xgb
from Chan import CChan
from ChanConfig import CChanConfig
from ChanModel.Features import CFeatures
from Common.CEnum import AUTYPE, DATA_SRC, KL_TYPE, BSP_TYPE
from Common.CTime import CTime
from Plot.PlotDriver import CPlotDriver
class T_SAMPLE_INFO(TypedDict):
feature: CFeatures
is_buy: bool
open_time: CTime
def plot(chan, plot_marker):
plot_config = {
"plot_kline": True,
"plot_bi": True,
"plot_seg": True,
"plot_zs": True,
"plot_bsp": True,
"plot_marker": True,
"plot_macd": True,
}
plot_para = {
"figure": {
"x_range": 400,
},
"marker": {
"markers": plot_marker
}
}
plot_driver = CPlotDriver(
chan,
plot_config=plot_config,
plot_para=plot_para,
)
plot_driver.save2img("label.png")
def stragety_feature(last_klu):
return {
"open_klu_rate": (last_klu.close - last_klu.open)/last_klu.open,
}
if __name__ == "__main__":
"""
本demo主要演示如何记录策略产出的买卖点的特征
然后将这些特征作为样本，训练一个模型(以XGB为demo)
用于预测买卖点的准确性
请注意，demo训练预测都用的是同一份数据，这是不合理的，仅仅是为了演示
"""
code = "BTC/USDT"
begin_time = "2024-12-1"
end_time = None
data_src = DATA_SRC.CCXT
lv_list = [KL_TYPE.K_1M]
config = CChanConfig({
"trigger_step": True, # 打开开关！
"bi_strict": True,
"skip_step": 0,
"divergence_rate": float("inf"),
"bsp2_follow_1": False,
"bsp3_follow_1": False,
"min_zs_cnt": 0,
"bs1_peak": False,
"macd_algo": "peak",
"bs_type": '1,2,3a,1p,2s,3b',
"print_warning": True,
"zs_algo": "normal",
})
chan = CChan(
code=code,
begin_time=begin_time,
end_time=end_time,
data_src=data_src,
lv_list=lv_list,
config=config,
autype=AUTYPE.QFQ,
)
bsp_dict: Dict[int, T_SAMPLE_INFO] = {} # 存储策略产出的bsp的特征
# 跑策略，保存买卖点的特征
index = 0
for chan_snapshot in chan.step_load():
last_klu = chan_snapshot[0][-1][-1]
bsp_list = chan_snapshot.get_bsp()
if not bsp_list:
continue
last_bsp = bsp_list[-1]
cur_lv_chan = chan_snapshot[0]
if last_bsp.klu.idx not in bsp_dict and cur_lv_chan[-2].idx == last_bsp.klu.klc.idx and last_bsp.is_buy:
# 假如策略是：买卖点分形第三元素出现时交易
bsp_dict[last_bsp.klu.idx] = {
"feature": last_bsp.features,
"is_buy": last_bsp.is_buy,
"open_time": last_klu.time,
}
bsp_dict[last_bsp.klu.idx]['feature'].add_feat(stragety_feature(last_klu)) # 开仓K线特征
print(index, last_bsp.klu.time, last_bsp.is_buy)
index += 1
# 生成libsvm样本特征
bsp_academy = [bsp.klu.idx for bsp in chan.get_bsp()]
feature_meta = {} # 特征meta
cur_feature_idx = 0
plot_marker = {}
fid = open("feature.libsvm", "w")
for bsp_klu_idx, feature_info in bsp_dict.items():
label = int(bsp_klu_idx in bsp_academy) # 以买卖点识别是否准确为label
features = [] # List[(idx, value)]
for feature_name, value in feature_info['feature'].items():
if feature_name not in feature_meta:
feature_meta[feature_name] = cur_feature_idx
cur_feature_idx += 1
features.append((feature_meta[feature_name], value))
features.sort(key=lambda x: x[0])
feature_str = " ".join([f"{idx}:{value}" for idx, value in features])
fid.write(f"{label} {feature_str}\n")
plot_marker[feature_info["open_time"].to_str()] = ("√" if label else "×", "down" if feature_info["is_buy"] else "up")
fid.close()
with open("feature.meta", "w") as fid:
# meta保存下来，实盘预测时特征对齐用
fid.write(json.dumps(feature_meta))
# 画图检查label是否正确
plot(chan, plot_marker)
# load sample file & train model
dtrain = xgb.DMatrix("feature.libsvm?format=libsvm") # load sample
param = {'max_depth': 2, 'eta': 0.3, 'objective': 'binary:logistic', 'eval_metric': 'auc'}
evals_result = {}
bst = xgb.train(
param,
dtrain=dtrain,
num_boost_round=50,
evals=[(dtrain, "train")],
evals_result=evals_result,
verbose_eval=True,
)
bst.save_model("buy_model.json")
# load model
model = xgb.Booster()
model.load_model("buy_model.json")
# predict
print(model.predict(dtrain))
Debug/strategy_demo6.py
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import json
from typing import Dict, TypedDict
import xgboost as xgb
from strategy_demo5 import stragety_feature
from BuySellPoint.BS_Point import CBS_Point
from Chan import CChan
from ChanConfig import CChanConfig
from ChanModel.Features import CFeatures
from Common.CEnum import AUTYPE, DATA_SRC, KL_TYPE
from Common.CTime import CTime
class T_SAMPLE_INFO(TypedDict):
feature: CFeatures
is_buy: bool
open_time: CTime
def predict_bsp(model: xgb.Booster, last_bsp: CBS_Point, meta: Dict[str, int]):
missing = -9999999
feature_arr = [missing] * len(meta)
for feat_name, feat_value in last_bsp.features.items():
if feat_name in meta:
feature_arr[meta[feat_name]] = feat_value
feature_arr = [feature_arr]
dtest = xgb.DMatrix(feature_arr, missing=missing)
return model.predict(dtest)
if __name__ == "__main__":
"""
本demo主要演示如何在实盘中把策略产出的买卖点，对接到demo5中训练好的离线模型上
"""
code = "BTC/USDT"
begin_time = "2023-11-30"
end_time = None
data_src = DATA_SRC.CCXT
lv_list = [KL_TYPE.K_DAY]
config = CChanConfig({
"trigger_step": True, # 打开开关！
})
chan = CChan(
code=code,
begin_time=begin_time,
end_time=end_time,
data_src=data_src,
lv_list=lv_list,
config=config,
autype=AUTYPE.QFQ,
)
model = xgb.Booster()
model.load_model("model.json")
meta = json.load(open("feature.meta", "r"))
treated_bsp_idx = set()
for chan_snapshot in chan.step_load():
# 策略逻辑要对齐demo5
last_klu = chan_snapshot[0][-1][-1]
bsp_list = chan_snapshot.get_bsp()
if not bsp_list:
continue
last_bsp = bsp_list[-1]
cur_lv_chan = chan_snapshot[0]
if last_bsp.klu.idx in treated_bsp_idx or cur_lv_chan[-2].idx != last_bsp.klu.klc.idx:
continue
last_bsp.features.add_feat(stragety_feature(last_klu)) # 开仓K线特征
# 买卖点打分，应该和demo5最后的predict结果完全一致才对
print(last_bsp.klu.time, predict_bsp(model, last_bsp, meta))
treated_bsp_idx.add(last_bsp.klu.idx)
Debug/xgboost_eval.py
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import json
from typing import Dict, TypedDict
import sys
import os
sys.path.append(os.path.abspath("/Users/jack/Project/chan.py"))
sys.path.append(os.path.abspath("/Users/jack/Project/chan.py/Debug"))
import xgboost as xgb
from BuySellPoint.BS_Point import CBS_Point
from Chan import CChan
from ChanConfig import CChanConfig
from ChanModel.Features import CFeatures
from Common.CEnum import AUTYPE, DATA_SRC, KL_TYPE, BSP_TYPE, MACD_ALGO
from Common.CTime import CTime
from Plot.PlotDriver import CPlotDriver
from Bi.Bi import CBi
from FeatureDict import FeatureDict
class T_SAMPLE_INFO(TypedDict):
feature: CFeatures
is_buy: bool
open_time: CTime
def plot(chan, plot_marker):
plot_config = {
"plot_kline": True,
"plot_bi": True,
"plot_seg": True,
"plot_zs": True,
"plot_bsp": True,
"plot_marker": True,
"plot_macd": True,
}
plot_para = {
"figure": {
"x_range": 4000,
},
"marker": {
"markers": plot_marker
}
}
plot_driver = CPlotDriver(
chan,
plot_config=plot_config,
plot_para=plot_para,
)
plot_driver.save2img("eval.png")
def predict_bsp(last_bsp: CBS_Point):
model = xgb.Booster()
if last_bsp.is_buy:
model.load_model("buy_model.json")
else:
model.load_model("sell_model.json")
meta = json.load(open("feature.meta", "r"))
missing = -9999999
feature_arr = [missing] * len(meta)
for feat_name, feat_value in last_bsp.features.items():
if feat_name in meta:
feature_arr[meta[feat_name]] = feat_value
feature_arr = [feature_arr]
dtest = xgb.DMatrix(feature_arr, missing=missing)
return model.predict(dtest)
if __name__ == "__main__":
"""
本demo主要演示如何记录策略产出的买卖点的特征
然后将这些特征作为样本，训练一个模型(以XGB为demo)
用于预测买卖点的准确性
请注意，demo训练预测都用的是同一份数据，这是不合理的，仅仅是为了演示
"""
code = "BTC/USDT:USDT"
begin_time = "2024-12-1"
end_time = "2024-12-20"
data_src = DATA_SRC.CCXT
lv_list = [KL_TYPE.K_1M]
config = CChanConfig({
"trigger_step": True, # 打开开关！
"bi_strict": True,
"skip_step": 0,
"divergence_rate": float("inf"),
"bsp2_follow_1": False,
"bsp3_follow_1": False,
"min_zs_cnt": 0,
"bs1_peak": False,
"macd_algo": "peak",
"bs_type": '1,2,3a,1p,2s,3b',
"print_warning": True,
"zs_algo": "normal",
"cal_rsi": True,
"cal_kdj": True,
})
chan = CChan(
code=code,
begin_time=begin_time,
end_time=end_time,
data_src=data_src,
lv_list=lv_list,
config=config,
autype=AUTYPE.QFQ,
)
bsp_dict: Dict[int, T_SAMPLE_INFO] = {} # 存储策略产出的bsp的特征
# 跑策略，保存买卖点的特征
index = 0
evals_count = 0
eval_klu_list = []
for chan_snapshot in chan.step_load():
last_klu = chan_snapshot[0][-1][-1]
bsp_list = chan_snapshot.get_bsp()
if not bsp_list:
continue
last_bsp = bsp_list[-1]
cur_lv_chan = chan_snapshot[0]
if BSP_TYPE.T1 in last_bsp.type or BSP_TYPE.T1P in last_bsp.type:
if last_bsp.klu.idx not in bsp_dict and cur_lv_chan[-2].idx == last_bsp.klu.klc.idx:
# 假如策略是：买卖点分形第三元素出现时交易
bsp_dict[last_bsp.klu.idx] = {
"feature": last_bsp.features,
"is_buy": last_bsp.is_buy,
"open_time": last_klu.time,
}
bsp_dict[last_bsp.klu.idx]['feature'].add_feat(FeatureDict().stragety_feature(last_bsp)) # 开仓K线特征
score = predict_bsp(last_bsp)
ok = ""
if score > 0.5:
ok = "OK"
evals_count = evals_count + 1
eval_klu_list.append(last_bsp.klu.idx)
print(index, last_bsp.klu.time, last_bsp.is_buy, score, ok)
index += 1
# 生成libsvm样本特征
bsp_academy = [bsp.klu.idx for bsp in chan.get_bsp()]
feature_meta = {} # 特征meta
cur_feature_idx = 0
plot_marker = {}
fid = open("eval_feature.libsvm", "w")
label_count = 0
for bsp_klu_idx, feature_info in bsp_dict.items():
label = int(bsp_klu_idx in bsp_academy) # 以买卖点识别是否准确为label
features = [] # List[(idx, value)]
for feature_name, value in feature_info['feature'].items():
if feature_name not in feature_meta:
feature_meta[feature_name] = cur_feature_idx
cur_feature_idx += 1
features.append((feature_meta[feature_name], value))
features.sort(key=lambda x: x[0])
feature_str = " ".join([f"{idx}:{value}" for idx, value in features])
fid.write(f"{label} {feature_str}\n")
plot_marker[feature_info["open_time"].to_str()] = ("√ "+ feature_info["open_time"].to_str() if label else "×", "down" if feature_info["is_buy"] else "up")
if label:
label_count = label_count + 1
fid.close()
print("Evals count: ", evals_count, "Lable count: ", label_count)
with open("feature.meta", "w") as fid:
# meta保存下来，实盘预测时特征对齐用
fid.write(json.dumps(feature_meta))
# 画图检查label是否正确
plot(chan, plot_marker)