Initial commit

2025-06-10 01:16:09 +08:00
commit 05bab03b8c
155 changed files with 19979 additions and 0 deletions
@@ -0,0 +1,77 @@
+
+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)