diff --git a/gogogo/result_visualizer.py b/gogogo/result_visualizer.py index 133e8a0..f9b223b 100644 --- a/gogogo/result_visualizer.py +++ b/gogogo/result_visualizer.py @@ -223,21 +223,9 @@ def print_trade_orders(strategy_data, stock_portfolio): print(f" 总订单数: {len(column_orders)}") print(f" 买入订单: {len(buy_orders)}") print(f" 卖出订单: {len(sell_orders)}") - print(f" 总交易金额: {column_orders['Size'].abs().sum():.2f} 股") + print(f" 总交易股数: {column_orders['Size'].abs().sum():.2f} 股") print(f" 总手续费: {column_orders['Fees'].sum():.2f}") - - # 按日期分组统计 - if time_column: - print(f"\n【按日期统计】") - daily_orders = orders_sorted.groupby(time_column).agg({ - 'Column': 'count', - 'Size': 'sum', - 'Fees': 'sum' - }).rename(columns={'Column': '订单数'}) - - print(daily_orders.to_string()) - else: - print(f"\n【按日期统计】- 未找到时间列") + # 重置pandas显示选项 pd.reset_option('display.max_rows') diff --git a/gogogo/strategy_executor.py b/gogogo/strategy_executor.py index aa7e6fe..e31ea12 100644 --- a/gogogo/strategy_executor.py +++ b/gogogo/strategy_executor.py @@ -7,6 +7,7 @@ from data_fetcher import get_processed_data def calculate_ratio_signals(ratio_series, window=20, num_std=2): """ 基于价格比率计算配对交易信号 + 优化信号逻辑，避免过于频繁的交易 """ # 计算布林带 ratio_ma = ratio_series.rolling(window=window).mean() @@ -16,25 +17,36 @@ def calculate_ratio_signals(ratio_series, window=20, num_std=2): lower_band = ratio_ma - num_std * ratio_std # 生成交易信号 - # 1: 做多价差 (买中芯/卖华虹) -> 买入比率 - # -1: 做空价差 (卖中芯/买华虹) -> 卖空比率 - # 0: 平仓 signals = pd.Series(0, index=ratio_series.index, name='signal') + current_signal = 0 - # 当比率突破下轨时做多价差 -> 买入比率 - long_condition = (ratio_series < lower_band) & (ratio_ma.notna()) - signals[long_condition] = 1 - - # 当比率突破上轨时做空价差 -> 卖空比率 - short_condition = (ratio_series > upper_band) & (ratio_ma.notna()) - signals[short_condition] = -1 - - # 当比率回归均值时平仓 - close_condition = (ratio_series.between(lower_band, upper_band)) & (signals.shift(1) != 0) - signals[close_condition] = 0 + for i in range(len(ratio_series)): + if i < window: # 跳过布林带计算期 + continue + + ratio_val = ratio_series.iloc[i] + ma_val = ratio_ma.iloc[i] + + # 当前无仓位时的开仓条件 + if current_signal == 0: + if ratio_val < lower_band.iloc[i]: # 比率突破下轨，做多价差 + signals.iloc[i] = 1 + current_signal = 1 + elif ratio_val > upper_band.iloc[i]: # 比率突破上轨，做空价差 + signals.iloc[i] = -1 + current_signal = -1 + + # 当前有仓位时的平仓条件 + elif current_signal != 0: + # 当比率回归均值时平仓 + if (current_signal == 1 and ratio_val >= ma_val) or \ + (current_signal == -1 and ratio_val <= ma_val): + signals.iloc[i] = 0 + current_signal = 0 return signals, ratio_ma, upper_band, lower_band + def generate_ratio_size(signals, price_ratio, position_size=0.5): """ 生成比率交易的size数据 @@ -69,13 +81,15 @@ def generate_ratio_size(signals, price_ratio, position_size=0.5): def generate_stock_sizes(signals, close_smic, close_hhic, initial_cash=100000, position_ratio=0.5): """ 生成真实股票交易的size数据 - 返回两个DataFrame：smic_size和hhic_size + 确保每次配对交易都是等市值对冲，避免使用杠杆 """ # 创建空的size Series smic_size = pd.Series(0.0, index=signals.index, name='SMIC') hhic_size = pd.Series(0.0, index=signals.index, name='HHIC') current_position = 0 # 0: 无仓位, 1: 做多价差, -1: 做空价差 + smic_position = 0.0 # 当前中芯持仓数量 + hhic_position = 0.0 # 当前华虹持仓数量 for i in range(len(signals)): if i < 20: # 跳过布林带计算期 @@ -85,44 +99,63 @@ def generate_stock_sizes(signals, close_smic, close_hhic, initial_cash=100000, p smic_price = close_smic.iloc[i] hhic_price = close_hhic.iloc[i] - if signal == 1 and current_position != 1: # 做多价差：买入中芯，卖空华虹 - # 计算每只股票的仓位价值 - position_value = initial_cash * position_ratio - - # 做多中芯国际 - smic_shares = position_value / smic_price - smic_size.iloc[i] = smic_shares - - # 做空华虹半导体 - hhic_shares = -position_value / hhic_price - hhic_size.iloc[i] = hhic_shares - - current_position = 1 - - elif signal == -1 and current_position != -1: # 做空价差：卖空中芯，买入华虹 - # 计算每只股票的仓位价值 - position_value = initial_cash * position_ratio - - # 做空中芯国际 - smic_shares = -position_value / smic_price - smic_size.iloc[i] = smic_shares - - # 做多华虹半导体 - hhic_shares = position_value / hhic_price - hhic_size.iloc[i] = hhic_shares - - current_position = -1 - - elif signal == 0 and current_position != 0: # 平仓 + # 平仓条件：信号为0且当前有仓位 + if signal == 0 and current_position != 0: # 平掉所有仓位 - if current_position == 1: # 平掉做多价差仓位 - smic_size.iloc[i] = -smic_size.shift(1).iloc[i] if i > 0 else 0 - hhic_size.iloc[i] = -hhic_size.shift(1).iloc[i] if i > 0 else 0 - elif current_position == -1: # 平掉做空价差仓位 - smic_size.iloc[i] = -smic_size.shift(1).iloc[i] if i > 0 else 0 - hhic_size.iloc[i] = -hhic_size.shift(1).iloc[i] if i > 0 else 0 + if smic_position != 0: + smic_size.iloc[i] = -smic_position + smic_position = 0.0 + if hhic_position != 0: + hhic_size.iloc[i] = -hhic_position + hhic_position = 0.0 current_position = 0 + continue + + # 开仓条件：只有当前无仓位时才开新仓 + if current_position == 0: + if signal == 1: # 做多价差：买入中芯，卖空华虹 + # 计算每只股票的仓位价值（等市值对冲） + position_value = initial_cash * position_ratio + + # 做多中芯国际 + smic_shares = position_value / smic_price + smic_size.iloc[i] = smic_shares + smic_position = smic_shares + + # 做空华虹半导体 + hhic_shares = -position_value / hhic_price + hhic_size.iloc[i] = hhic_shares + hhic_position = hhic_shares + + current_position = 1 + print(f"开仓做多价差: {signals.index[i]} - 买入SMIC {smic_shares:.2f}股 @{smic_price:.2f}, 卖空HHIC {abs(hhic_shares):.2f}股 @{hhic_price:.2f}") + + elif signal == -1: # 做空价差：卖空中芯，买入华虹 + # 计算每只股票的仓位价值（等市值对冲） + position_value = initial_cash * position_ratio + + # 做空中芯国际 + smic_shares = -position_value / smic_price + smic_size.iloc[i] = smic_shares + smic_position = smic_shares + + # 做多华虹半导体 + hhic_shares = position_value / hhic_price + hhic_size.iloc[i] = hhic_shares + hhic_position = hhic_shares + + current_position = -1 + print(f"开仓做空价差: {signals.index[i]} - 卖空SMIC {abs(smic_shares):.2f}股 @{smic_price:.2f}, 买入HHIC {hhic_shares:.2f}股 @{hhic_price:.2f}") + + # 最后检查是否有未平仓的仓位，如果有则在最后一天平仓 + if current_position != 0: + last_index = len(signals) - 1 + if smic_position != 0: + smic_size.iloc[last_index] = -smic_position + if hhic_position != 0: + hhic_size.iloc[last_index] = -hhic_position + print(f"最终平仓: {signals.index[last_index]} - 平掉所有剩余仓位") # 创建size DataFrame size_df = pd.DataFrame({