下面这段代码是否有错误，或者不是最新格式：for i in range(len(df_stock)): # 判断是否买入 if df_stock.loc[i, 'buy_signal'] == 1 and bought == 0: buy_price = df_stock.loc[i, 'close'] # 以当天收盘价买入 df_stock.loc[i, 'bought'] = 1 # 标记已购买 bought = 1

这段代码没有明显的错误，但是可以进行一些改进。以下是改进的建议：

for i in range(len(df_stock)):
    # 判断是否买入
    if df_stock.at[i, 'buy_signal'] == 1 and bought == 0:
        buy_price = df_stock.at[i, 'close']  # 以当天收盘价买入
        df_stock.at[i, 'bought'] = 1  # 标记已购买
        bought = 1

改进的地方包括：
- 使用`at`方法替代`loc`方法，因为`at`方法更适合单个元素的访问，可以提高代码的执行效率。
- 将索引操作`[]`改为`at`方法，以避免返回Series对象而直接返回标量值。

请注意，这只是对代码进行了一些细微的改进，如果代码在其他方面有问题，还需要根据具体情况进行修改。

相关问题

import pandas as pd window_size = 4 # 定义窗口大小 df = pd.read_csv('stock_data.csv') if len(df) >= window_size and window_size > 0: df['four_days_increase'] = df['close'].rolling(window=window_size).apply(lambda x: all(x[i] < x[i+1] for i in range(window_size-1))) * 1 if len(df) >= window_size-1 and window_size > 0: df['three_days_decrease'] = df['close'].rolling(window=window_size-1).apply(lambda x: all(x[i] > x[i+1] for i in range(window_size-2))) * 1修改此代码，避免出现0 is not in range

如果你仍然遇到 `0 is not in range` 的错误，可以尝试以下两种方法：

1. 检查数据中是否有缺失值。如果数据中有缺失值，可能会导致rolling函数中的窗口大小小于指定的窗口大小，从而导致错误。你可以使用 `df.isnull().sum()` 来检查数据中是否有缺失值。

2. 将lambda函数中的 `range(window_size-1)` 和 `range(window_size-2)` 改为 `range(window_size)` 和 `range(window_size-1)`，这样可以确保范围中包含数字0。

修改后的代码如下：

import pandas as pd 

window_size = 4 # 定义窗口大小 
df = pd.read_csv('stock_data.csv') 

if len(df) >= window_size and window_size > 0: 
    df['four_days_increase'] = df['close'].rolling(window=window_size).apply(lambda x: all(x[i] < x[i+1] for i in range(window_size))) * 1 

if len(df) >= window_size-1 and window_size > 0: 
    df['three_days_decrease'] = df['close'].rolling(window=window_size-1).apply(lambda x: all(x[i] > x[i+1] for i in range(window_size-1))) * 1

注意，这里仅仅是对错误的解决方案，如果是数据问题，需要对数据进行处理。

import pandas as pd df = pd.read_csv('stock_data.csv') df['four_days_increase'] = df['close'].rolling(window=4).apply(lambda x: all(x[i] < x[i+1] for i in range(3))) * 1 df['three_days_decrease'] = df['close'].rolling(window=3).apply(lambda x: all(x[i] > x[i+1] for i in range(2))) * 1 capital = 1000000 max_stock_per_day = 10 max_stock_value = 100000 start_date = '2020-01-01' end_date = '2023-01-01' df = df[(df['date'] >= start_date) & (df['date'] < end_date)] df = df.reset_index(drop=True) hold_stock = [] for i, row in df.iterrows(): if len(hold_stock) > 0: sell_stock = [] for stock in hold_stock: if i - stock['buy_day'] >= 3: capital += stock['buy_price'] * stock['buy_qty'] * (1 - 0.002) sell_stock.append(stock) hold_stock = [stock for stock in hold_stock if stock not in sell_stock] df_today = df.loc[i:i+3] if i + 3 >= len(df): break if all(df_today['four_days_increase']) and all(df_today['three_days_decrease'].iloc[1:]): available_capital = capital available_stock = max_stock_per_day available_value = max_stock_value for j, stock_row in df_today.iterrows(): if available_capital > 0 and available_stock > 0 and available_value > 0: buy_qty = min(int(available_capital / (stock_row['close'] * 1.002)), available_stock, int(available_value / (stock_row['close'] * 1.002))) if buy_qty > 0: hold_stock.append({'buy_day': i, 'buy_price': stock_row['close'], 'buy_qty': buy_qty}) available_capital -= stock_row['close'] * buy_qty * 1.002 available_stock -= 1 available_value -= stock_row['close'] * buy_qty * 1.002 print('Final capital:', capital)让上述代码在jupyter里不报错

这段代码的错误可能是由于缺少依赖库或文件。请确保您已经安装了pandas库并且已经将stock_data.csv文件放在正确的位置。另外，您可能需要在Jupyter Notebook中使用`%matplotlib inline`来确保图表正确显示。以下是可能的修改：

import pandas as pd 
%matplotlib inline

df = pd.read_csv('stock_data.csv') 
df['four_days_increase'] = df['close'].rolling(window=4).apply(lambda x: all(x[i] < x[i+1] for i in range(3))) * 1 
df['three_days_decrease'] = df['close'].rolling(window=3).apply(lambda x: all(x[i] > x[i+1] for i in range(2))) * 1 

capital = 1000000 
max_stock_per_day = 10 
max_stock_value = 100000 
start_date = '2020-01-01' 
end_date = '2023-01-01' 

df = df[(df['date'] >= start_date) & (df['date'] < end_date)] 
df = df.reset_index(drop=True) 

hold_stock = [] 

for i, row in df.iterrows(): 
    if len(hold_stock) > 0: 
        sell_stock = [] 
        for stock in hold_stock: 
            if i - stock['buy_day'] >= 3: 
                capital += stock['buy_price'] * stock['buy_qty'] * (1 - 0.002) 
                sell_stock.append(stock) 
        hold_stock = [stock for stock in hold_stock if stock not in sell_stock] 

    df_today = df.loc[i:i+3] 

    if i + 3 >= len(df): 
        break 

    if all(df_today['four_days_increase']) and all(df_today['three_days_decrease'].iloc[1:]): 
        available_capital = capital 
        available_stock = max_stock_per_day 
        available_value = max_stock_value 
        for j, stock_row in df_today.iterrows(): 
            if available_capital > 0 and available_stock > 0 and available_value > 0: 
                buy_qty = min(int(available_capital / (stock_row['close'] * 1.002)), available_stock, int(available_value / (stock_row['close'] * 1.002))) 
                if buy_qty > 0: 
                    hold_stock.append({'buy_day': i, 'buy_price': stock_row['close'], 'buy_qty': buy_qty}) 
                    available_capital -= stock_row['close'] * buy_qty * 1.002 
                    available_stock -= 1 
                    available_value -= stock_row['close'] * buy_qty * 1.002 

print('Final capital:', capital)