使用 python 调整 yfinance 的推荐变量

Question

如何使用 python 中的 yfinance 库调整变量建议以达成共识建议，例如指定的月份数、间隔和时间段？

例如，下面的脚本将提供从当前日期算起的 4 个月、1 个月的间隔。

import pandas as pd  # Import the missing "pd" module
import yfinance as yf

# Define the stock ticker
stock = yf.Ticker("AAPL")

# Get stock info
info = stock.info

# Access analyst recommendations if available
recommendations = stock.recommendations

print(recommendations)  # Print the full DataFrame

输出

  period  strongBuy  buy  hold  sell  strongSell
0     0m         11   21     6     0           0
1    -1m         10   17    12     2           0
2    -2m         10   17    12     2           0
3    -3m         10   24     7     1           0

但是尝试使用脚本来指定日期、月份或间隔，然后提供更多时间段

例如，periods=12，interval = 2 个月，end=mm/dd/yyyy 将提供 12 个周期，总共可追溯到 24 个月。

我的尝试遇到了一系列错误

import pandas as pd
import yfinance as yf
from datetime import datetime, timedelta

def get_analyst_recommendations(stock_ticker, num_months, interval_months, end_date=None):
    if end_date is None:
        end_date = datetime.now().strftime("%Y-%m-%d")
    else:
        end_date = datetime.strptime(end_date, "%m/%d/%Y").strftime("%Y-%m-%d")

    start_date = (datetime.strptime(end_date, "%Y-%m-%d") - timedelta(days=num_months*30)).strftime("%Y-%m-%d")

    stock = yf.Ticker(stock_ticker)
    
    if hasattr(stock, 'recommendations'):
        recommendations = stock.recommendations
        if recommendations is None or recommendations.empty:
            print(f"No analyst recommendations found for {stock_ticker}")
            return None
    else:
        print(f"Recommendations data not available for {stock_ticker}")
        return None

    recommendations = recommendations.loc[start_date:end_date]

    # Convert the index to a DatetimeIndex
    recommendations.index = pd.to_datetime(recommendations.index)

    # Resample the recommendations based on the specified interval
    resampled_recommendations = recommendations.resample(f'{interval_months}ME', label='right').agg('last')

    # Count the number of each recommendation grade
    grade_counts = resampled_recommendations.value_counts().unstack(fill_value=0)

    # Reset the index
    grade_counts = grade_counts.reset_index()

    # Convert the relevant column to datetime and format it as a string
    grade_counts['your_datetime_column'] = pd.to_datetime(grade_counts['your_datetime_column']).dt.strftime('-%mME')

    # Set the formatted column as the index
    grade_counts = grade_counts.set_index('your_datetime_column')

    grade_counts.index = grade_counts.index.strftime('-%mME')

    # Rename the columns to match the desired output format
    grade_counts.columns = grade_counts.columns.str.replace(' ', '')
    grade_counts = grade_counts.reindex(columns=['strongBuy', 'Buy', 'Hold', 'Sell', 'strongSell']).fillna(0).astype(int)

    # Add the '0m' row at the beginning
    grade_counts.loc['0m'] = 0
    grade_counts = grade_counts.sort_index(ascending=False)

    return grade_counts

# Example usage
stock_ticker = "AAPL"
num_months = 24
interval_months = 2
end_date = "05/06/2024"

recommendations = get_analyst_recommendations(stock_ticker, num_months, interval_months, end_date)
print(recommendations)

Answer 1

你把事情搞得太复杂了一点。我建议采用这种方法：

import pandas as pd
import yfinance as yf
from datetime import datetime, timedelta

def get_analyst_recommendations(stock_ticker, num_periods, interval_months, end_date=None):
    if end_date is None:
        end_date = datetime.now()
    else:
        end_date = datetime.strptime(end_date, "%m/%d/%Y")

    start_date = end_date - timedelta(days=interval_months * 30 * num_periods)

    stock = yf.Ticker(stock_ticker)
    recommendations = stock.recommendations

    current_date = end_date
    recommendations['Date'] = [current_date - pd.DateOffset(months=i) for i in range(len(recommendations))]
    recommendations.set_index('Date', inplace=True)
    recommendations = recommendations[(recommendations.index >= start_date) & (recommendations.index <= end_date)]
    if recommendations.empty:
        print("No data in the specified date range:", start_date, "to", end_date)
        return None

    resampled_recommendations = recommendations.resample(f'{interval_months}M').last()

    return resampled_recommendations

stock = yf.Ticker("AAPL")

recommendations = stock.recommendations
print("Current Analyst Recommendations:")
print(recommendations)

custom_recommendations = get_analyst_recommendations('AAPL', num_periods=12, interval_months=2, end_date='05/07/2024')
print("Custom Time-based Analyst Recommendations:")
print(custom_recommendations)

这会给你（结合普通推荐）

Current Analyst Recommendations:
  period  strongBuy  buy  hold  sell  strongSell
0     0m         11   21     6     0           0
1    -1m         10   17    12     2           0
2    -2m         10   17    12     2           0
3    -3m         10   24     7     1           0
Custom Time-based Analyst Recommendations:
           period  strongBuy  buy  hold  sell  strongSell
Date                                                     
2024-02-29    -3m         10   24     7     1           0
2024-04-30    -1m         10   17    12     2           0
2024-06-30     0m         11   21     6     0           0