在计算 tr 计算前收盘价时遇到困难

Question

• 我正在尝试排除故障并理解为什么第一天[2024-03-27]打印了tr2和tr3的值，而它应该打印nan nan，因为2024-03-27没有前一天

• 我只希望它根据仅打印的天数计算数据

我最初认为shift(-1)可能有问题，但仍然可能存在问题。不过，正因为如此，计算才正确。

我还认为 API 收集的数据可能超过 4 天，这可以解释为什么没有 nan nan。

这是一个示例输出：

Date       Open   High   Low    Close  TR1  TR2  TR3
2024-03-27 5.8100 6.0100 5.4200 5.9300 0.59 0.2 0.39 ( the first day should be nan nan)
2024-03-28 5.9400 6.3200 5.8301 5.8900 0.49 0.39 0.1
2024-04-01 5.9100 5.9400 5.5500 5.5600 0.39 0.05 0.34
2024-04-02 5.2800 5.4500 5.0900 5.3800 0.36 0.11 0.47

我的脚本：

import requests
import pandas as pd
from datetime import datetime, timedelta
import time


# Calculate start and end dates for daily historical data
end_date_daily = datetime.now().strftime('%Y-%m-%d')
start_date_daily = (datetime.now() - timedelta(days=4)).strftime('%Y-%m-%d')

# Construct the API URL for daily historical data
api_url_daily = f'https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol={symbol}&apikey={API_KEY}&datatype=json&start_date={start_date_daily}&end_date={end_date_daily}'

# Fetch and process daily historical price data
response_daily = requests.get(api_url_daily)

if response_daily.status_code == 200:
    historical_data_daily = response_daily.json()['Time Series (Daily)']

    # Convert daily historical data to DataFrame
    df_daily = pd.DataFrame(historical_data_daily).T
    df_daily.index = pd.to_datetime(df_daily.index)

    # Reverse the DataFrame to have oldest data at the top
    df_daily = df_daily.sort_index(ascending=False)

    # Convert column types to float
    df_daily['High'] = df_daily['2. high'].astype(float)
    df_daily['Low'] = df_daily['3. low'].astype(float)
    df_daily['Close'] = df_daily['4. close'].astype(float)


    # Function to calculate tr1, tr2, tr3
    def calculate_tr(df):
        df['tr1'] = df['High'] - df['Low']
        df['tr2'] = abs(df['High'] - df['Close'].shift(-1))
        df['tr3'] = abs(df['Low'] - df['Close'].shift(-1))

    # Calculate tr1, tr2, tr3
    calculate_tr(df_daily)

    # Print DataFrame after calculating tr1, tr2, tr3
    print("\nDataFrame after calculating tr1, tr2, tr3:")
    print("Date       Open   High   Low    Close  TR1  TR2  TR3")
    for date in reversed(df_daily.head(4).index):
        row = df_daily.loc[date]
        tr1_rounded = round(row['tr1'], 4)
        tr2_rounded = round(row['tr2'], 4)
        tr3_rounded = round(row['tr3'], 4)
        print(f"{date.strftime('%Y-%m-%d')} {row['1. open']} {row['2. high']} {row['3. low']} {row['4. close']} {round(tr1_rounded, 3)} {round(tr2_rounded, 3)} {round(tr3_rounded, 3)}")

 

Answer 1

IIUC，你可以这样做：

def calculate_tr(df):
    high_shifted, close_shifted, low_shifted = (
        df["High"].shift(),
        df["Close"].shift(),
        df["Low"].shift(),
    )

    df["tr1"] = df["High"] - df["Low"]
    df["tr2"] = (high_shifted - close_shifted).abs()
    df["tr3"] = (low_shifted - close_shifted).abs()


calculate_tr(df_daily)
print(df_daily)

打印：

         Date  Open  High     Low  Close     tr1   tr2     tr3
0  2024-03-27  5.81  6.01  5.4200   5.93  0.5900   NaN     NaN
1  2024-03-28  5.94  6.32  5.8301   5.89  0.4899  0.08  0.5100
2  2024-04-01  5.91  5.94  5.5500   5.56  0.3900  0.43  0.0599
3  2024-04-02  5.28  5.45  5.0900   5.38  0.3600  0.38  0.0100