更新俩chatgpt写的策略

examples/demo-project/strategy/ChatGPTStrategy.py

@@ -0,0 +1,77 @@
+'''
+我理解您对先前策略的担忧。让我们尝试一个不同的策略，这次我们将使用动量和反转因子结合市值和流动性指标，来寻找可能的投资机会。该策略将试图捕捉那些近期表现良好且具备合理市值和流动性的股票，同时也考虑到那些可能即将反转的股票。
+
+以下是新策略的大致框架：
+
+动量和反转因子：使用例如12个月的价格变动率作为动量因子，以及近期的价格下跌作为可能的反转信号。
+市值和流动性：选取市值较大且交易量健康的股票，以确保流动性和稳定性。
+技术指标：使用如MACD等技术指标来辅助判断市场趋势和交易时机。
+以下是根据这些原则修改的策略代码：
+
+'''
+import datetime
+import os
+import random
+from finhack.factor.default.factorManager import factorManager
+from finhack.market.astock.astock import AStock
+
+# 初始化函数，设定股票、基准等
+def initialize(context):
+    set_benchmark('000001.SH')
+    set_option('use_real_price', True)
+    set_option('order_volume_ratio', 1)
+    set_order_cost(OrderCost(open_tax=0, close_tax=0.001, open_commission=0.0003, close_commission=0.0003, close_today_commission=0, min_commission=5), type='stock')
+    set_slippage(PriceRelatedSlippage(0.00246), type='stock')
+
+    g.stocknum = 10
+    g.days = 0
+    g.refresh_rate = 10
+    run_daily(trade, time="09:30")
+    log.info('Initial setup completed')
+
+    g.factors = factorManager.getFactors(['MACD_0', 'RSI_14_0', 'ATR_14_0', 'totalMv_0', 'volumeRatio_0', 'pe_0', 'pb_0'])
+    g.factors=g.factors.reset_index()
+
+def trade(context):
+    if g.days % g.refresh_rate == 0:
+        sell_all_stocks(context)
+        cash_per_stock = context.portfolio.cash / g.stocknum
+
+        now_date = context.current_dt.strftime('%Y%m%d')
+        df = g.factors.query("trade_date == '{}'".format(now_date))
+
+        # 多因子筛选逻辑
+        df = filter_stocks(df)
+        stock_list = df.head(g.stocknum)['ts_code'].tolist()
+
+        # 买入股票
+        buy_stocks(context, stock_list, cash_per_stock)
+
+        g.days = 1
+    else:
+        g.days += 1
+
+def sell_all_stocks(context):
+    sell_list = list(context.portfolio.positions.keys()) 
+    if len(sell_list) > 0 :
+        for stock in sell_list:
+            order_target_value(stock, 0)  
+
+def buy_stocks(context, stock_list, cash_per_stock):
+    for stock in stock_list:
+        if len(context.portfolio.positions.keys()) < g.stocknum:
+            order_value(stock, cash_per_stock)
+
+def filter_stocks(df):
+    # 应用筛选条件
+    filtered_df = df.copy()
+    filtered_df = filtered_df[filtered_df['MACD_0'] > 0]  # 正MACD值，表明上升趋势
+    filtered_df = filtered_df[filtered_df['RSI_14_0'] < 70]  # RSI值低于70，避免超买状态
+    filtered_df = filtered_df[filtered_df['totalMv_0'] > 1e5]  # 选择市值较大的股票
+    filtered_df = filtered_df[filtered_df['volumeRatio_0'] > 1]  # 成交量比率大于1
+    filtered_df = filtered_df[(filtered_df['pe_0'] > 0) & (filtered_df['pe_0'] < 50)]  # 合理的市盈率范围
+    filtered_df = filtered_df[(filtered_df['pb_0'] > 0) & (filtered_df['pb_0'] < 2)]  # 合理的市净率范围
+
+    # 根据MACD值排序
+    filtered_df = filtered_df.sort_values(by='MACD_0', ascending=False)
+    return filtered_df

examples/demo-project/strategy/ChatGPTStrategy1.py

@@ -0,0 +1,73 @@
+'''
+理解您的要求，我将为您提供一个更复杂和专业的量化交易策略，这将基于您提供的股票因子，并适应您的量化回测框架。这个策略将侧重于多因子选择股票，并结合动量和价值因子，同时考虑股票的市值大小和技术指标，以期实现更好的交易表现。
+
+这个策略的基本思路是：
+使用多个因子（如市盈率、市净率、市值、MACD等）综合评估股票。
+选择表现最佳的股票进行买入，并定期调整持仓。
+利用技术指标作为辅助决策工具，如使用均线判断市场趋势。
+以下是完整的示例策略代码：
+
+'''
+import datetime
+import os
+import random
+from finhack.factor.default.factorManager import factorManager
+from finhack.market.astock.astock import AStock
+
+# 初始化函数，设定股票、基准等
+def initialize(context):
+    set_benchmark('000001.SH')
+    set_option('use_real_price', True)
+    set_option('order_volume_ratio', 1)
+    set_order_cost(OrderCost(open_tax=0, close_tax=0.001, open_commission=0.0003, close_commission=0.0003, close_today_commission=0, min_commission=5), type='stock')
+    set_slippage(PriceRelatedSlippage(0.00246), type='stock')
+
+    g.stocknum = 10
+    g.days = 0
+    g.refresh_rate = 10
+    run_daily(trade, time="09:30")
+    log.info('Initial setup completed')
+
+    g.factors = factorManager.getFactors(['pe_0', 'MACD_0', 'peTtm_0', 'pb_0', 'totalMv_0', 'MA_90_0','close_0'])
+    g.factors=g.factors.reset_index()
+
+def trade(context):
+    if g.days % g.refresh_rate == 0:
+        sell_all_stocks(context)
+        cash_per_stock = context.portfolio.cash / g.stocknum
+
+        now_date = context.current_dt.strftime('%Y%m%d')
+        df = g.factors.query("trade_date == '{}'".format(now_date))
+
+        # 多因子筛选逻辑
+        df = filter_stocks(df)
+        stock_list = df.head(g.stocknum)['ts_code'].tolist()
+
+        # 买入股票
+        buy_stocks(context, stock_list, cash_per_stock)
+
+        g.days = 1
+    else:
+        g.days += 1
+
+def sell_all_stocks(context):
+    sell_list = list(context.portfolio.positions.keys()) 
+    if len(sell_list) > 0 :
+        for stock in sell_list:
+            order_target_value(stock, 0)  
+
+def buy_stocks(context, stock_list, cash_per_stock):
+    for stock in stock_list:
+        if len(context.portfolio.positions.keys()) < g.stocknum:
+            order_value(stock, cash_per_stock)
+
+def filter_stocks(df):
+    # 基于市盈率、市净率、MACD、市值和90日均线的简单多因子模型
+    df_filtered = df[df['pe_0'] > 0]
+    df_filtered = df_filtered[df_filtered['pb_0'] < 2]
+    df_filtered = df_filtered[df_filtered['MACD_0'] < 0]
+    df_filtered = df_filtered[df_filtered['peTtm_0'] < df_filtered['pe_0']]
+    df_filtered = df_filtered[df_filtered['totalMv_0'] > 1e5]  # 筛选市值较大的股票
+    df_filtered = df_filtered[df_filtered['close_0'] > df_filtered['MA_90_0']]  # 当前价格高于90日均线
+    df_filtered = df_filtered.sort_values(by='totalMv_0', ascending=True)
+    return df_filtered

examples/demo-project/strategy/SmallCapStrategy.py

@@ -55,7 +55,8 @@ def trade(context):
 
         ## 选股
         now_date=context.current_dt.strftime('%Y%m%d')
-        df = g.factors.query(f"trade_date =='{now_date}' & pe_0 > 0 & pb_0 < 2 & MACD_0<0 & peTtm_0<pe_0 & pe_0<50 & pe_0>10")
+        df = g.factors[g.factors.trade_date==now_date]
+        df = g.factors.query(f"pe_0 > 0 & pb_0 < 2 & MACD_0<0 & peTtm_0<pe_0 & pe_0<50 & pe_0>10")
         df=df.sort_values(by='totalMv_0',ascending=True, inplace=False) 
 
         stock_list = df.head(g.stocknum)['ts_code'].tolist()

finhack/trader/default/function.py

@@ -187,12 +187,12 @@ def order_value(security, value, style=None, side='long', pindex=0, close_today=
         order_sell(security,amount)
 
 
-#目标股数下单
-def order_target(security, amount, style=None, side='long', pindex=0, close_today=False):
-    if amount>0:
-        order_buy(security,amount)
-    else:
-        order_sell(security,amount)
+# #目标股数下单
+# def order_target(security, amount, style=None, side='long', pindex=0, close_today=False):
+#     if amount>0:
+#         order_buy(security,amount)
+#     else:
+#         order_sell(security,amount)
 
 
 #目标价值下单