提交基础内容

.idea/.gitignore

@@ -0,0 +1,8 @@
+# 默认忽略的文件
+/shelf/
+/workspace.xml
+# 基于编辑器的 HTTP 客户端请求
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml

.idea/inspectionProfiles/Project_Default.xml

@@ -0,0 +1,19 @@
+<component name="InspectionProjectProfileManager">
+  <profile version="1.0">
+    <option name="myName" value="Project Default" />
+    <inspection_tool class="DuplicatedCode" enabled="true" level="WEAK WARNING" enabled_by_default="true">
+      <Languages>
+        <language minSize="74" name="Python" />
+      </Languages>
+    </inspection_tool>
+    <inspection_tool class="PyPackageRequirementsInspection" enabled="true" level="WARNING" enabled_by_default="true">
+      <option name="ignoredPackages">
+        <value>
+          <list size="1">
+            <item index="0" class="java.lang.String" itemvalue="pandas" />
+          </list>
+        </value>
+      </option>
+    </inspection_tool>
+  </profile>
+</component>

.idea/inspectionProfiles/profiles_settings.xml

@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/misc.xml

@@ -0,0 +1,7 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Black">
+    <option name="sdkName" value="Python 3.10 (土信号)" />
+  </component>
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10 (土信号)" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/土信号.iml" filepath="$PROJECT_DIR$/.idea/土信号.iml" />
+    </modules>
+  </component>
+</project>

.idea/土信号.iml

@@ -0,0 +1,10 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$">
+      <excludeFolder url="file://$MODULE_DIR$/venv" />
+    </content>
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

土-优化01策略.txt

@@ -0,0 +1,41 @@
+X_1:=LLV(LOW,10);
+X_2:=HHV(HIGH,25);
+X_5:=3.5;
+X_6:=REF(EMA((CLOSE-X_1)/(X_2-X_1)*4,4),1);
+X_7:=IF(EXIST(CROSS(X_6,X_5),5),0,1);
+X_10:=(REF(CLOSE,1)-REF(CLOSE,2))/REF(CLOSE,2)*100;
+X_11:=2*(REF(CLOSE+HIGH+LOW,1)+OPEN)*100;
+X_12:=(X_11/EMA(X_11,4)-1)*100;
+X_13:=X_7*X_12;
+X_14:=DMA(EMA(OPEN,12),SUM(REF(VOL,1),5)/3/CAPITAL);
+X_15:=X_7*(OPEN-X_14)/X_14*200;
+X_16:=0-(HHV(EMA(OPEN,5),14)-OPEN)/OPEN*5000;
+X_17:=REF(CLOSE+LOW+3*OPEN+HIGH,1)/6;
+X_18:=20*X_17+19*REF(X_17,1)+18*REF(X_17,2)+17*REF(X_17,3)+16*REF(X_17,4)+15*REF(X_17,5)+14*REF(X_17,6)+13*REF(X_17,7)+12*REF(X_17,8)+11*REF(X_17,9);
+X_19:=10*REF(X_17,10)+9*REF(X_17,11)+8*REF(X_17,12)+7*REF(X_17,13)+6*REF(X_17,14)+5*REF(X_17,15)+4*REF(X_17,16)+3*REF(X_17,17)+2*REF(X_17,18)+REF(X_17,20);
+X_20:=(X_18+X_19)/210;
+X_21:=MA(X_20,6);
+X_22:=(X_20-X_21)*1.2;
+X_23:=(OPEN*2+REF(HIGH+LOW,1))/4*10;
+X_24:=EMA(X_23,6)-EMA(X_23,55);
+X_25:=EMA(X_24,6);
+X_26:=(X_24-X_25)*0.06;
+X_27:=X_22+X_26;
+X_28:=X_27>0;
+X_29:=MA(OPEN,3);
+X_30:=MA(OPEN,13);
+X_31:=MA(OPEN,34);
+X_32:=X_29>X_30 AND X_31>=REF(X_31,1);
+X_33:=EMA(OPEN,12);
+X_34:=EMA(OPEN,50);
+X_35:=X_33-X_34>0;
+X_36:=OPEN/REF(CLOSE,1)-1;
+X_37:=IF(X_32,2,0)+IF(X_28,2,0)+IF(X_35,2,0)+X_36;
+X_38:=(X_7*X_37/10+X_16/1000)*100+25;
+X_39:=X_13+X_15+X_38;
+X_40:=NOT(NAMELIKE(313) OR NAMELIKE(314) OR NAMELIKE(315) OR VOL=0);
+X_41:=SLOPE(X_39,2);
+X_42:=SLOPE(X_6,2);
+X_43:=SUM(X_10,2)*X_7;
+
+土:IF(X_43>8 AND X_40,X_41*0.02-X_42*X_7,DRAWNULL)*X_7,COLORCYAN;

土-原始策略.txt

@@ -0,0 +1,52 @@
+X_1:=LLV(LOW,10);
+X_2:=HHV(HIGH,25);
+X_4:=3.2;
+X_5:=3.5;
+X_6:=REF(EMA((CLOSE-X_1)/(X_2-X_1)*4,4),1);
+X_7:=IF(EXIST(CROSS(X_6,X_5),5),0,1);
+X_10:=(REF(CLOSE,1)-REF(CLOSE,2))/REF(CLOSE,2)*100;
+X_11:=2*(REF(CLOSE+HIGH+LOW,1)+OPEN)*100;
+X_12:=(X_11/EMA(X_11,4)-1)*100;
+X_13:=X_7*X_12;
+X_14:=DMA(EMA(OPEN,12),SUM(REF(VOL,1),5)/3/CAPITAL);
+X_15:=X_7*(OPEN-X_14)/X_14*200;
+X_16:=0-(HHV(EMA(OPEN,5),14)-OPEN)/OPEN*5000;
+X_17:=REF(CLOSE+LOW+3*OPEN+HIGH,1)/6;
+X_18:=20*X_17+19*REF(X_17,1)+18*REF(X_17,2)+17*REF(X_17,3)+16*REF(X_17,4)+15*REF(X_17,5)+14*REF(X_17,6)+13*REF(X_17,7)+12*REF(X_17,8)+11*REF(X_17,9);
+X_19:=10*REF(X_17,10)+9*REF(X_17,11)+8*REF(X_17,12)+7*REF(X_17,13)+6*REF(X_17,14)+5*REF(X_17,15)+4*REF(X_17,16)+3*REF(X_17,17)+2*REF(X_17,18)+REF(X_17,20);
+X_20:=(X_18+X_19)/210;
+X_21:=MA(X_20,6);
+X_22:=(X_20-X_21)*1.2;
+X_23:=(OPEN*2+REF(HIGH+LOW,1))/4*10;
+X_24:=EMA(X_23,6)-EMA(X_23,55);
+X_25:=EMA(X_24,6);
+X_26:=(X_24-X_25)*0.06;
+X_27:=X_22+X_26;
+X_28:=X_27>0;
+X_29:=MA(OPEN,3);
+X_30:=MA(OPEN,13);
+X_31:=MA(OPEN,34);
+X_32:=X_29>X_30 AND X_31>=REF(X_31,1);
+X_33:=EMA(OPEN,12);
+X_34:=EMA(OPEN,50);
+X_35:=X_33-X_34>0;
+X_36:=OPEN/REF(CLOSE,1)-1;
+X_37:=IF(X_32,2,0)+IF(X_28,2,0)+IF(X_35,2,0)+X_36;
+X_38:=(X_7*X_37/10+X_16/1000)*100+25;
+X_39:=X_13+X_15+X_38;
+量比:= VOL / REF(MA(VOL,5),1);
+X_44:= REF(VOL,1)>0 AND 量比>1;
+X_40:= NOT(NAMELIKE(313) OR NAMELIKE(314) OR NAMELIKE(315)) AND REF(VOL,1)>0 AND VOL>0;
+X_41:=SLOPE(X_39,2);
+X_42:=SLOPE(X_6,2);
+X_43:=SUM(X_10,2)*X_7;
+
+
+
+ZB100:=REF(CLOSE,1)>REF(CLOSE,2)*1.09 AND REF(HIGH,1)=REF(CLOSE,1);
+今开%:(OPEN-REF(CLOSE,1))/REF(CLOSE,1)*100;
+土:IF(X_43>8 AND X_40,X_41*0.02-X_42*X_7,DRAWNULL)*X_7,COLORCYAN;
+昨板数:BARSLASTCOUNT(ZB100),COLORRED;
+细分行业:DRAWTEXT(1,0,HYBLOCK),COLOR00C0C0;
+
+

土策略回测_基础01.py

@@ -0,0 +1,158 @@
+import os
+import pandas as pd
+import numpy as np
+from datetime import datetime, timedelta
+
+# 回测配置
+DATA_PATH = r'D:\gp_data\day'  # 日线数据存放路径
+START_DATE = '20150101'  # 回测开始日期
+END_DATE = '20231231'  # 回测结束日期
+STOP_LOSS = 0.03  # 止损比例（3%）
+COMMISSION = 0.0003  # 单边交易佣金（万分之三）
+
+
+# 策略信号计算函数
+def calculate_signal(df):
+    """计算土信号"""
+    df = df.copy()
+
+    # 计算基础参数
+    df['X_1'] = df['low'].rolling(10, min_periods=1).min()  # 10日最低价
+    df['X_2'] = df['high'].rolling(25, min_periods=1).max()  # 25日最高价
+    df['X_5'] = 3.5  # 固定阈值
+
+    # 标准化波动率（X_6）
+    df['X_6'] = ((df['close'] - df['X_1']) / (df['X_2'] - df['X_1']) * 4)
+    df['X_6'] = df['X_6'].ewm(span=4, adjust=False).mean().shift(1)  # 4日EMA并滞后1日
+
+    # 波动率过滤（X_7）
+    df['cross_flag'] = (df['X_6'] > df['X_5']).astype(int)  # 当前是否突破
+    df['cross_flag_shift'] = df['cross_flag'].shift(1)  # 前一日状态
+    df['cross_event'] = (df['cross_flag'] > df['cross_flag_shift']).astype(int)  # 上穿事件
+    df['X_7'] = df['cross_event'].rolling(5).max().replace(1, 0).shift(1)  # 过去5日有上穿则为0
+
+    # 动量计算（X_43）
+    df['X_10'] = (df['close'].shift(1) - df['close'].shift(2)) / df['close'].shift(2) * 100
+    df['X_43'] = df['X_10'].rolling(2).sum() * df['X_7']
+
+    # 复合指标（简化版X_39）
+    ema_open_12 = df['open'].ewm(span=12, adjust=False).mean()
+    df['X_15'] = df['X_7'] * (df['open'] - ema_open_12) / ema_open_12 * 200
+
+    # 斜率计算
+    def calc_slope(series):
+        if len(series) < 2: return np.nan
+        return np.polyfit([0, 1], series.values, 1)[0]
+
+    df['X_41'] = df['X_15'].rolling(2).apply(calc_slope, raw=False)
+    df['X_42'] = df['X_6'].rolling(2).apply(calc_slope, raw=False)
+
+    # 生成最终信号
+    df['signal'] = np.where(
+        (df['X_43'] > 8) &
+        (~df['ts_code'].str.startswith(('313', '314', '315'))) &  # 排除ST
+        (df['vol'] > 0) &  # 排除零成交量
+        (df['X_7'] == 1),  # 波动率过滤
+        (df['X_41'] * 0.02 - df['X_42'] * df['X_7']) * df['X_7'],
+        np.nan
+    )
+    return df
+
+
+# 回测引擎核心逻辑
+def backtest_strategy():
+    all_trades = []
+
+    # 遍历所有股票文件
+    for filename in os.listdir(DATA_PATH):
+        if not filename.endswith('_daily_data.txt'):
+            continue
+
+        # 读取数据
+        code = filename.split('_')[0]
+        df = pd.read_csv(
+            os.path.join(DATA_PATH, filename),
+            sep='\t',
+            parse_dates=['trade_date'],
+            dtype={'ts_code': str}
+        )
+
+        # 过滤日期范围
+        df = df[(df['trade_date'] >= pd.to_datetime(START_DATE)) &
+                (df['trade_date'] <= pd.to_datetime(END_DATE))]
+        if df.empty:
+            continue
+
+        # 按日期排序并计算信号
+        df = df.sort_values('trade_date')
+        df = calculate_signal(df)
+
+        # 提取有效信号
+        signals = df[df['signal'].notna()]
+
+        # 逐笔交易处理
+        for idx, row in signals.iterrows():
+            buy_date = row['trade_date']
+            buy_price = row['open']
+
+            # 获取次日数据
+            next_day = df[df['trade_date'] > buy_date].head(1)
+            if next_day.empty:
+                continue  # 无次日数据（如停牌）
+
+            sell_date = next_day['trade_date'].values[0]
+            sell_high = next_day['high'].values[0]
+            sell_low = next_day['low'].values[0]
+
+            # 计算止损价
+            stop_price = buy_price * (1 - STOP_LOSS)
+
+            # 确定卖出价格
+            if sell_low < stop_price:
+                sell_price = stop_price  # 触发止损
+            else:
+                sell_price = sell_high  # 按次日最高价卖出
+
+            # 计算收益率（扣除双边佣金）
+            ret = (sell_price / buy_price - 1) - 2 * COMMISSION
+            success = 1 if ret > 0 else 0
+
+            # 记录交易
+            all_trades.append({
+                'code': code,
+                'buy_date': buy_date.strftime('%Y%m%d'),
+                'sell_date': sell_date.strftime('%Y%m%d'),
+                'buy_price': buy_price,
+                'sell_price': sell_price,
+                'return': ret,
+                'success': success,
+                'hold_days': 1
+            })
+
+    # 绩效分析
+    if not all_trades:
+        print("未产生任何交易信号")
+        return
+
+    results = pd.DataFrame(all_trades)
+
+    # 关键指标计算
+    total_trades = len(results)
+    win_rate = results['success'].mean()
+    avg_return = results['return'].mean()
+    annualized_return = avg_return * 240  # 假设年240个交易日
+
+    # 输出结果
+    print(f"回测期间：{START_DATE} 至 {END_DATE}")
+    print(f"总交易次数：{total_trades}")
+    print(f"胜率：{win_rate:.2%}")
+    print(f"单次平均收益率：{avg_return:.2%}")
+    print(f"年化收益率（估算）：{annualized_return:.2%}")
+
+    # 保存详细结果
+    results.to_csv('trading_records.csv', index=False)
+    print("详细交易记录已保存至 trading_records.csv")
+
+
+if __name__ == '__main__':
+    backtest_strategy()