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我正在尝试使用 mplfinance.plot() 针对特定股票的图绘制股票市场交易。我使用用作 CSV 文件的 jstock 记录我的所有交易：

"Code","Symbol","Date","Units","Purchase Price","Current Price","Purchase Value","Current Value","Gain/Loss Price","Gain/Loss Value","Gain/Loss %","Broker","Clearing Fee","Stamp Duty","Net Purchase Value","Net Gain/Loss Value","Net Gain/Loss %","Comment"
"ASO","Academy Sports and Outdoors, Inc.","Sep 13, 2021","25.0","45.85","46.62","1146.25","1165.5","0.769999999999996","19.25","1.6793893129770994","0.0","0.0","0.0","1146.25","19.25","1.6793893129770994",""
"ASO","Academy Sports and Outdoors, Inc.","Aug 26, 2021","15.0","41.3","46.62","619.5","699.3","5.32","79.79999999999995","12.881355932203384","0.0","0.0","0.0","619.5","79.79999999999995","12.881355932203384",""
"ASO","Academy Sports and Outdoors, Inc.","Jun 3, 2021","10.0","37.48","46.62","374.79999999999995","466.2","9.14","91.40000000000003","24.386339381003214","0.0","0.0","0.0","374.79999999999995","91.40000000000003","24.386339381003214",""
"RMBS","Rambus Inc.","Nov 24, 2021","2.0","26.99","26.99","53.98","53.98","0.0","0.0","0.0","0.0","0.0","0.0","53.98","0.0","0.0",""

我可以很容易地使用这些数据

myportfolio = pd.read_csv(PORTFOLIO_LOCATION, parse_dates=[2])

但我需要为每笔交易创建与每日股价相匹配的单独列表：

Date,High,Low,Open,Close,Volume,Adj Close
2020-12-01,17.020000457763672,16.5,16.799999237060547,16.8799991607666,990900,16.8799991607666
2020-12-02,17.31999969482422,16.290000915527344,16.65999984741211,16.40999984741211,1200500,16.40999984741211

我有一个包含这个的普通 DataFrame。到目前为止，这就是我所拥有的：

for i in myportfolio.groupby("Code"):
    (code, j) = i
    if code == "ASO": # just testing it against one stock
        simp = pd.DataFrame(columns=["Date", "Units", "Price"],
            data=j[["Date", "Units", "Purchase Price"]].values, index=j[["Date"]])
        df = pd.read_csv("ASO-2020-12-01-2021-12-01.csv", index_col=0, parse_dates=True)
        # df.lookup(simp["Date"])
        df.insert(0, 'row_num', range(0,len(df)))
        k = df.loc[simp["Date"]]['row_num']
        trades = []
        for index, m in k.iteritems():
            t = np.zeros((df.shape[0], 1))
            t.fill(np.nan)
            t[m] = simp[index]["Price"]
            trades.append(t.to_list())

但我收到一个KeyError: Timestamp('2021-09-17 00:00:00')

关于如何解决这个问题的任何想法？

附录 1：

import pandas as pd
trade_data = [['ASO', '5/5/21', 10], ['ASO', '5/6/21', 12], ['RBLX', '5/7/21', 15]]
trade_df = pd.DataFrame(trade_data, columns = ['Code', 'Date', 'Price'])
trade_df['Date'] = pd.to_datetime(trade_df['Date'])
trade_df

    Code    Date    Price
0   ASO 2021-05-05  10
1   ASO 2021-05-07  12
2   RBLX    2021-05-07  15

aso_data = [['5/5/21', 12, 5, 10, 7], ['5/6/21', 15, 7, 13, 8], ['5/7/21', 17, 10, 15, 11]]
aso_df = pd.DataFrame(aso_data, columns = ['Date', 'High', 'Low', 'Open', 'Close'])
aso_df['Date'] = pd.to_datetime(aso_df['Date'])
aso_df

    Date    High    Low Open    Close
0   2021-05-05  12  5   10  7
1   2021-05-06  15  7   13  8
2   2021-05-07  17  10  15  11

所以我想为 ASO 创建两个 NumPy 数组（每个交易一个）和一个 RBLX 交易。对于 ASO，我应该有两个看起来像[10, Nan, Nan]和的 NumPy 数组[NaN, NaN, 12]。

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2 回答 2

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你想要一个列表列表吗？无需循环。

df_list = df.values.tolist()
于 2021-12-01T18:59:51.707 回答
0

以防万一像我这样的另一个新手遇到类似的问题。

for i in myportfolio.groupby(["Code"]):
    (code, j) = i
    if code == "ASO": # just testing it against one stock
        df = pd.read_csv("ASO-2020-12-01-2021-12-01.csv", index_col=0, parse_dates=True)
        df.insert(0, 'row_num', range(0,len(df)))
        k = df.loc[j["Date"]]['row_num']
        trades = []
        for index, m in j.iterrows(): 
            t = np.zeros((df.shape[0], 1))
            t.fill(np.nan)
            t[int(df.loc[m["Date"]]['row_num'])] = m["Purchase Price"]
            asplot = mpf.make_addplot(t, type="scatter", color='red', marker="D")
            trades.append(asplot)
        mpf.plot(df, type='candle', addplot=trades)

生成了一个很好的图表，显示了我的入口点。祝你好运带有红色菱形的股票价格图表显示我在哪里以及何时购买股票

于 2021-12-09T11:34:23.597 回答