prev_returns_copy = prev_returns_copy.astype(np.int64)
print(prev_returns_copy)
return prev_returns_copy
project_tests.test_get_top_n(get_top_n)
# ### View Data
# We want to get the best performing and worst performing stocks. To get the best performing stocks, we'll use the `get_top_n` function. To get the worst performing stocks, we'll also use the `get_top_n` function. However, we pass in `-1*prev_returns` instead of just `prev_returns`. Multiplying by negative one will flip all the positive returns to negative and negative returns to positive. Thus, it will return the worst performing stocks.
# In[42]:
top_bottom_n = 50
df_long = get_top_n(prev_returns, top_bottom_n)
df_short = get_top_n(-1 * prev_returns, top_bottom_n)
project_helper.print_top(df_long, 'Longed Stocks')
project_helper.print_top(df_short, 'Shorted Stocks')
# ## Projected Returns
# It's now time to check if your trading signal has the potential to become profitable!
#
# We'll start by computing the net returns this portfolio would return. For simplicity, we'll assume every stock gets an equal dollar amount of investment. This makes it easier to compute a portfolio's returns as the simple arithmetic average of the individual stock returns.
#
# Implement the `portfolio_returns` function to compute the expected portfolio returns. Using `df_long` to indicate which stocks to long and `df_short` to indicate which stocks to short, calculate the returns using `lookahead_returns`. To help with calculation, we've provided you with `n_stocks` as the number of stocks we're investing in a single period.
# In[47]:
def portfolio_returns(df_long, df_short, lookahead_returns, n_stocks):
"""
Compute expected returns for the portfolio, assuming equal investment in each long/short stock.
previous_top.append(date_values[top_n - 1])
return (prev_returns >= previous_top).astype(np.int)
project_tests.test_get_top_n(get_top_n)
# ### View Data
# We want to get the best performing and worst performing stocks. To get the best performing stocks, we'll use the `get_top_n` function. To get the worst performing stocks, we'll also use the `get_top_n` function. However, we pass in `-1*prev_returns` instead of just `prev_returns`. Multiplying by negative one will flip all the positive returns to negative and negative returns to positive. Thus, it will return the worst performing stocks.
# In[13]:
top_bottom_n = 50
df_long = get_top_n(prev_returns, top_bottom_n)
df_short = get_top_n(-1 * prev_returns, top_bottom_n)
project_helper.print_top(df_long, "Longed Stocks")
project_helper.print_top(df_short, "Shorted Stocks")
# ## Projected Returns
# It's now time to check if your trading signal has the potential to become profitable!
#
# We'll start by computing the net returns this portfolio would return. For simplicity, we'll assume every stock gets an equal dollar amount of investment. This makes it easier to compute a portfolio's returns as the simple arithmetic average of the individual stock returns.
#
# Implement the `portfolio_returns` function to compute the expected portfolio returns. Using `df_long` to indicate which stocks to long and `df_short` to indicate which stocks to short, calculate the returns using `lookahead_returns`. To help with calculation, we've provided you with `n_stocks` as the number of stocks we're investing in a single period.
# In[15]:
def portfolio_returns(df_long, df_short, lookahead_returns, n_stocks):
"""
Compute expected returns for the portfolio, assuming equal investment in each long/short stock.