Question

import yfinance as yf

try:
    data = yf.download(tickers=all_symbols[:50], start=start, end=end, group_by="ticker")
except:
    pass

类似的东西应该隐藏了yfinance抛出的所有异常，但是当没有找到股票代号时，我总是会在Jupyter笔记本上发现一些异常。我如何让它们停止出现？

Exception in thread Thread-333:
Traceback (most recent call last):
  File "/Users/jason/anaconda3/lib/python3.7/site-packages/yfinance/__init__.py", line 313, in history
    quotes = self._parse_quotes(data["chart"]["result"][0])
  File "/Users/jason/anaconda3/lib/python3.7/site-packages/yfinance/__init__.py", line 162, in _parse_quotes
    timestamps = data["timestamp"]
KeyError: 'timestamp'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/Users/jason/anaconda3/lib/python3.7/threading.py", line 917, in _bootstrap_inner
    self.run()
  File "/Users/jason/anaconda3/lib/python3.7/threading.py", line 865, in run
    self._target(*self._args, **self._kwargs)
  File "/Users/jason/anaconda3/lib/python3.7/site-packages/multitasking/__init__.py", line 102, in _run_via_pool
    return callee(*args, **kwargs)
  File "/Users/jason/anaconda3/lib/python3.7/site-packages/yfinance/__init__.py", line 470, in _download_one_threaded
    period, interval, prepost, proxy)
  File "/Users/jason/anaconda3/lib/python3.7/site-packages/yfinance/__init__.py", line 483, in _download_one
    proxy=proxy)
  File "/Users/jason/anaconda3/lib/python3.7/site-packages/yfinance/__init__.py", line 316, in history
    raise ValueError(self.ticker, err_msg)
ValueError: ('ACCP', 'No data found for this date range, symbol may be delisted')

Answer 1

异常位于单独的线程中，这就是为什么您不捕获并禁止它们的原因。这也是为什么其余结果继续正确下载的原因。

您的主要问题是您的数据不正确，而修复该错误数据将解决您的问题。 ACCP还没有进行首次公开募股，它可能至少有3年没有发行过。

您有几种选择：

找到更准确的股票代号来源。
运行时线程数为False，或者一次运行一次，遇到错误时将停止运行，然后删除错误的符号并继续。由于它不是多线程的，因此会减慢该过程。
在第一次运行时忽略错误，然后在数据中查找丢失的内容，然后删除这些符号以备将来运行。
编辑您的本地yfinance以处理_download_one_threaded中的异常并排除该错误。

Answer 2

这个怎么样？你可以用这个做吗？

# https://towardsdatascience.com/efficient-frontier-portfolio-optimisation-in-python-e7844051e7f
import pandas as pd  
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import quandl
import scipy.optimize
np.random.seed(777)


quandl.ApiConfig.api_key = 'you_resy_goes_here'
stocks = ['AAPL', 'AMZN', 'MSFT', 'SBUX']
data = quandl.get_table('WIKI/PRICES', ticker = stocks,
                        qopts = { 'columns': ['date', 'ticker', 'adj_close'] },
                        date = { 'gte': '2018-1-1', 'lte': '2019-12-31' }, paginate=True)
data.head()

data.info()

df = data.set_index('date')
table = df.pivot(columns='ticker')
# By specifying col[1] in below list comprehension
# You can select the stock names under multi-level column
table.columns = [col[1] for col in table.columns]
table.head()

plt.figure(figsize=(14, 7))
for c in table.columns.values:
    plt.plot(table.index, table[c], lw=3, alpha=0.8,label=c)
plt.legend(loc='upper left', fontsize=12)
plt.ylabel('price in $')


returns = table.pct_change()
plt.figure(figsize=(14, 7))
for c in returns.columns.values:
    plt.plot(returns.index, returns[c], lw=3, alpha=0.8,label=c)
plt.legend(loc='upper right', fontsize=12)
plt.ylabel('daily returns')


###################################################


def portfolio_annualised_performance(weights, mean_returns, cov_matrix):
    returns = np.sum(mean_returns*weights ) *252
    std = np.sqrt(np.dot(weights.T, np.dot(cov_matrix, weights))) * np.sqrt(252)
    return std, returns

def random_portfolios(num_portfolios, mean_returns, cov_matrix, risk_free_rate):
    results = np.zeros((3,num_portfolios))
    weights_record = []
    for i in range(num_portfolios):
        weights = np.random.random(4)
        weights /= np.sum(weights)
        weights_record.append(weights)
        portfolio_std_dev, portfolio_return = portfolio_annualised_performance(weights, mean_returns, cov_matrix)
        results[0,i] = portfolio_std_dev
        results[1,i] = portfolio_return
        results[2,i] = (portfolio_return - risk_free_rate) / portfolio_std_dev
    return results, weights_record


returns = table.pct_change()
mean_returns = returns.mean()
cov_matrix = returns.cov()
num_portfolios = 25000
risk_free_rate = 0.0178


###################################################


def display_simulated_ef_with_random(mean_returns, cov_matrix, num_portfolios, risk_free_rate):
    results, weights = random_portfolios(num_portfolios,mean_returns, cov_matrix, risk_free_rate)

    max_sharpe_idx = np.argmax(results[2])
    sdp, rp = results[0,max_sharpe_idx], results[1,max_sharpe_idx]
    max_sharpe_allocation = pd.DataFrame(weights[max_sharpe_idx],index=table.columns,columns=['allocation'])
    max_sharpe_allocation.allocation = [round(i*100,2)for i in max_sharpe_allocation.allocation]
    max_sharpe_allocation = max_sharpe_allocation.T

    min_vol_idx = np.argmin(results[0])
    sdp_min, rp_min = results[0,min_vol_idx], results[1,min_vol_idx]
    min_vol_allocation = pd.DataFrame(weights[min_vol_idx],index=table.columns,columns=['allocation'])
    min_vol_allocation.allocation = [round(i*100,2)for i in min_vol_allocation.allocation]
    min_vol_allocation = min_vol_allocation.T

    print("-")
    print("Maximum Sharpe Ratio Portfolio Allocation\n")
    print("Annualised Return:", round(rp,2))
    print("Annualised Volatility:", round(sdp,2))
    print("\n")
    print(max_sharpe_allocation)
    print("-")
    print("Minimum Volatility Portfolio Allocation\n")
    print("Annualised Return:", round(rp_min,2))
    print("Annualised Volatility:", round(sdp_min,2))
    print("\n")
    print(min_vol_allocation)

    plt.figure(figsize=(10, 7))
    plt.scatter(results[0,:],results[1,:],c=results[2,:],cmap='YlGnBu', marker='o', s=10, alpha=0.3)
    plt.colorbar()
    plt.scatter(sdp,rp,marker='*',color='r',s=500, label='Maximum Sharpe ratio')
    plt.scatter(sdp_min,rp_min,marker='*',color='g',s=500, label='Minimum volatility')
    plt.title('Simulated Portfolio Optimization based on Efficient Frontier')
    plt.xlabel('annualised volatility')
    plt.ylabel('annualised returns')
    plt.legend(labelspacing=0.8)


display_simulated_ef_with_random(mean_returns, cov_matrix, num_portfolios, risk_free_rate)

Answer 3

问题固定在0.1.46。所有下载完成后，将打印错误。

如何抑制来自金融的例外？

3 个答案: