# TODO: Implement function
return (prices - prices.shift(1)) / prices.shift(1)
project_tests.test_generate_returns(generate_returns)
# ### View Data
# Let's generate the closing returns using `generate_returns` and view them using a heatmap.
# In[ ]:
returns = generate_returns(close)
project_helper.plot_returns(returns, "Close Returns")
# ## Weighted Returns
# With the returns of each stock computed, we can use it to compute the returns for an index or ETF. Implement `generate_weighted_returns` to create weighted returns using the returns and weights.
# In[ ]:
def generate_weighted_returns(returns, weights):
"""
Generate weighted returns.
Parameters
----------
returns : DataFrame
# TODO: Implement Function
prev_mon_prices = prices.shift(1)
returns = np.log(prices) - np.log(prev_mon_prices)
return returns
project_tests.test_compute_log_returns(compute_log_returns)
# ### View Data
# Using the same data returned from `resample_prices`, we'll generate the log returns.
# In[10]:
monthly_close_returns = compute_log_returns(monthly_close)
project_helper.plot_returns(
monthly_close_returns.loc[:, apple_ticker],
'Log Returns of {} Stock (Monthly)'.format(apple_ticker))
# ## Shift Returns
# Implement the `shift_returns` function to shift the log returns to the previous or future returns in the time series. For example, the parameter `shift_n` is 2 and `returns` is the following:
#
# ```
# Returns
# A B C D
# 2013-07-08 0.015 0.082 0.096 0.020 ...
# 2013-07-09 0.037 0.095 0.027 0.063 ...
# 2013-07-10 0.094 0.001 0.093 0.019 ...
# 2013-07-11 0.092 0.057 0.069 0.087 ...
# ... ... ... ... ...
# ```
#
The returns for each ticker and date
"""
#TODO: Implement function
return (prices / prices.shift(1) - 1)
project_tests.test_generate_returns(generate_returns)
# ### View Data
# Let's generate the closing returns using `generate_returns` and view them using a heatmap.
# In[52]:
returns = generate_returns(close)
project_helper.plot_returns(returns, 'Close Returns')
# ## Weighted Returns
# With the returns of each stock computed, we can use it to compute the returns for an index or ETF. Implement `generate_weighted_returns` to create weighted returns using the returns and weights.
# In[80]:
def generate_weighted_returns(returns, weights):
"""
Generate weighted returns.
Parameters
----------
returns : DataFrame
Returns for each ticker and date