diff --git a/src/.keep b/src/.keep
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diff --git a/src/FixedRatioStrategyFill_V2.py b/src/FixedRatioStrategyFill_V2.py
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+++ b/src/FixedRatioStrategyFill_V2.py
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+from re import X
+import numpy as np
+import pandas as pd
+
+from core.model.strategy_config import PosStrategyConfig
+
+
+def calc_ratio(equity_dfs: list[pd.DataFrame], stg_conf: PosStrategyConfig) -> pd.DataFrame:
+    """
+    计算选币仓位结果，只接受两个参数，1. 资金曲线们，2. 策略配置
+    :param equity_dfs: 资金曲线，是一个df的列表，里面包含配置中要求计算好的因子
+    :param stg_conf: 策略配置
+    :return: 返回仓位比
+    """
+    # 取出对应参数
+    cap_ratios = stg_conf.params['cap_ratios']  # 默认资金分配
+    pos_limit = stg_conf.params['pos_limit']
+    monitor_strategy = pos_limit['monitor_strategy']  # 监控的策略
+    
+    # 获取各种情况下的资金分配方案
+    alt_ratios_0 = pos_limit.get('alt_ratios_0', cap_ratios)  # 空仓时的资金分配
+    alt_ratios_1 = pos_limit.get('alt_ratios_1', cap_ratios)  # 选1个币时的资金分配
+    alt_ratios_2 = pos_limit.get('alt_ratios_2', cap_ratios)  # 选2个币时的资金分配
+    alt_ratios_3 = pos_limit.get('alt_ratios_3', cap_ratios)  # 选3个币时的资金分配
+    alt_ratios_4 = pos_limit.get('alt_ratios_4', cap_ratios)  # 选4个币时的资金分配
+    alt_ratios_5plus = pos_limit.get('alt_ratios_5plus', cap_ratios)  # 选5个及以上币时的资金分配
+
+    monitor_df = None
+    monitor_index = None
+
+    print(f"策略列表长度: {len(stg_conf.strategy_cfg_list)}")
+    for i, cfg in enumerate(stg_conf.strategy_cfg_list):
+        print(f"策略{i}: {cfg.name}")
+
+    # 找到需要监控的策略
+    for index, cfg in enumerate(stg_conf.strategy_cfg_list):
+        if cfg.name.endswith(monitor_strategy):
+            equity_df = pd.read_csv(cfg.get_result_folder() / '资金曲线.csv', encoding='utf-8-sig', parse_dates=['candle_begin_time'])
+            monitor_df = equity_df
+            monitor_index = index
+            break
+    
+    if monitor_df is None:
+        print(f"未找到监控策略: {monitor_strategy}，使用默认资金分配...")
+        
+    # 判断资金占比的参数数量是否与资金曲线的数量一致
+    all_ratios = [cap_ratios, alt_ratios_0, alt_ratios_1, alt_ratios_2, alt_ratios_3, alt_ratios_4, alt_ratios_5plus]
+    for ratios in all_ratios:
+        if len(ratios) != len(equity_dfs):
+            print(f'资金比例数量与资金曲线数量不同，请检查配置: {ratios}')
+            exit()
+
+    # 构建时间轴
+    begin_times = equity_dfs[0]['candle_begin_time']
+
+    # 创建一个全 0 的 NDArray
+    cap_weights = np.zeros([len(begin_times), len(equity_dfs)])
+
+    # 分配默认权重
+    for i in range(len(cap_ratios)):
+        cap_weights[:, i] = cap_ratios[i]
+
+    # 根据监控策略的选币数量动态调整资金分配
+    if monitor_df is not None:
+        # 创建选币数量掩码
+        long_num = monitor_df['symbol_long_num'].values
+        short_num = monitor_df['symbol_short_num'].values
+        
+        # 确保长度匹配
+        if len(long_num) != len(begin_times):
+            print('警告：监控策略的时间轴与其他策略不一致，可能导致结果不准确')
+            # 创建DataFrame时需要转换index类型
+            return pd.DataFrame(cap_weights, columns=range(cap_weights.shape[1]), index=begin_times)
+        
+        # 计算总选币数量
+        total_num = np.add(long_num, short_num)  # 使用numpy的add函数
+        
+        # 创建不同选币数量的掩码
+        mask_0 = (total_num == 0)  # 空仓
+        mask_1 = (total_num == 1)  # 选1个币
+        mask_2 = (total_num == 2)  # 选2个币
+        mask_3 = (total_num == 3)  # 选3个币
+        mask_4 = (total_num == 4)  # 选4个币
+        mask_5plus = (total_num >= 5)  # 选5个及以上币
+        
+        # 根据不同选币数量应用不同的资金分配方案
+        for i in range(len(equity_dfs)):
+            # 空仓情况
+            cap_weights[mask_0, i] = alt_ratios_0[i]
+            # 选1个币情况
+            cap_weights[mask_1, i] = alt_ratios_1[i]
+            # 选2个币情况
+            cap_weights[mask_2, i] = alt_ratios_2[i]
+            # 选3个币情况
+            cap_weights[mask_3, i] = alt_ratios_3[i]
+            # 选4个币情况
+            cap_weights[mask_4, i] = alt_ratios_4[i]
+            # 选5个及以上币情况
+            cap_weights[mask_5plus, i] = alt_ratios_5plus[i]
+
+    # 将结果转换回 DataFrame，注意index可能需要转换
+    cap_weights = pd.DataFrame(cap_weights, columns=range(cap_weights.shape[1]), index=begin_times)
+
+    return cap_weights
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