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jackyu66git
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import json
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from typing import Dict, TypedDict
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import sys
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import os
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sys.path.append(os.path.abspath("/Users/jack/Project/chan.py"))
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sys.path.append(os.path.abspath("/Users/jack/Project/chan.py/Debug"))
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import xgboost as xgb
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from BuySellPoint.BS_Point import CBS_Point
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from Chan import CChan
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from ChanConfig import CChanConfig
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from ChanModel.Features import CFeatures
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from Common.CEnum import AUTYPE, DATA_SRC, KL_TYPE, BSP_TYPE, MACD_ALGO
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from Common.CTime import CTime
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from Plot.PlotDriver import CPlotDriver
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from Bi.Bi import CBi
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from FeatureDict import FeatureDict
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class T_SAMPLE_INFO(TypedDict):
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feature: CFeatures
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is_buy: bool
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open_time: CTime
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def plot(chan, plot_marker):
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plot_config = {
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"plot_kline": True,
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"plot_bi": True,
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"plot_seg": True,
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"plot_zs": True,
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"plot_bsp": True,
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"plot_marker": True,
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"plot_macd": True,
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}
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plot_para = {
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"figure": {
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"x_range": 4000,
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},
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"marker": {
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"markers": plot_marker
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}
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}
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plot_driver = CPlotDriver(
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chan,
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plot_config=plot_config,
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plot_para=plot_para,
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)
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plot_driver.save2img("eval.png")
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def predict_bsp(last_bsp: CBS_Point):
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model = xgb.Booster()
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if last_bsp.is_buy:
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model.load_model("buy_model.json")
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else:
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model.load_model("sell_model.json")
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meta = json.load(open("feature.meta", "r"))
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missing = -9999999
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feature_arr = [missing] * len(meta)
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for feat_name, feat_value in last_bsp.features.items():
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if feat_name in meta:
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feature_arr[meta[feat_name]] = feat_value
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feature_arr = [feature_arr]
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dtest = xgb.DMatrix(feature_arr, missing=missing)
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return model.predict(dtest)
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if __name__ == "__main__":
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"""
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本demo主要演示如何记录策略产出的买卖点的特征
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然后将这些特征作为样本，训练一个模型(以XGB为demo)
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用于预测买卖点的准确性
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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
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"""
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code = "BTC/USDT:USDT"
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begin_time = "2024-12-1"
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end_time = "2024-12-20"
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data_src = DATA_SRC.CCXT
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lv_list = [KL_TYPE.K_1M]
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config = CChanConfig({
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"trigger_step": True, # 打开开关！
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"bi_strict": True,
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"skip_step": 0,
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"divergence_rate": float("inf"),
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"bsp2_follow_1": False,
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"bsp3_follow_1": False,
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"min_zs_cnt": 0,
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"bs1_peak": False,
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"macd_algo": "peak",
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"bs_type": '1,2,3a,1p,2s,3b',
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"print_warning": True,
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"zs_algo": "normal",
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"cal_rsi": True,
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"cal_kdj": True,
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})
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chan = CChan(
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code=code,
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begin_time=begin_time,
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end_time=end_time,
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data_src=data_src,
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lv_list=lv_list,
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config=config,
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autype=AUTYPE.QFQ,
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)
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bsp_dict: Dict[int, T_SAMPLE_INFO] = {} # 存储策略产出的bsp的特征
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# 跑策略，保存买卖点的特征
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index = 0
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evals_count = 0
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eval_klu_list = []
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for chan_snapshot in chan.step_load():
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last_klu = chan_snapshot[0][-1][-1]
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bsp_list = chan_snapshot.get_bsp()
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if not bsp_list:
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continue
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last_bsp = bsp_list[-1]
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cur_lv_chan = chan_snapshot[0]
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if BSP_TYPE.T1 in last_bsp.type or BSP_TYPE.T1P in last_bsp.type:
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if last_bsp.klu.idx not in bsp_dict and cur_lv_chan[-2].idx == last_bsp.klu.klc.idx:
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# 假如策略是：买卖点分形第三元素出现时交易
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bsp_dict[last_bsp.klu.idx] = {
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"feature": last_bsp.features,
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"is_buy": last_bsp.is_buy,
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"open_time": last_klu.time,
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}
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bsp_dict[last_bsp.klu.idx]['feature'].add_feat(FeatureDict().stragety_feature(last_bsp)) # 开仓K线特征
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score = predict_bsp(last_bsp)
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ok = ""
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if score > 0.5:
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ok = "OK"
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evals_count = evals_count + 1
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eval_klu_list.append(last_bsp.klu.idx)
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print(index, last_bsp.klu.time, last_bsp.is_buy, score, ok)
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index += 1
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# 生成libsvm样本特征
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bsp_academy = [bsp.klu.idx for bsp in chan.get_bsp()]
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feature_meta = {} # 特征meta
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cur_feature_idx = 0
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plot_marker = {}
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fid = open("eval_feature.libsvm", "w")
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label_count = 0
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for bsp_klu_idx, feature_info in bsp_dict.items():
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label = int(bsp_klu_idx in bsp_academy) # 以买卖点识别是否准确为label
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features = [] # List[(idx, value)]
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for feature_name, value in feature_info['feature'].items():
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if feature_name not in feature_meta:
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feature_meta[feature_name] = cur_feature_idx
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cur_feature_idx += 1
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features.append((feature_meta[feature_name], value))
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features.sort(key=lambda x: x[0])
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feature_str = " ".join([f"{idx}:{value}" for idx, value in features])
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fid.write(f"{label} {feature_str}\n")
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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")
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if label:
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label_count = label_count + 1
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fid.close()
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print("Evals count: ", evals_count, "Lable count: ", label_count)
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with open("feature.meta", "w") as fid:
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# meta保存下来，实盘预测时特征对齐用
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fid.write(json.dumps(feature_meta))
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# 画图检查label是否正确
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plot(chan, plot_marker)
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