# Import Price Table as Pandas DataFrame from Pickle File for S&P500 + Discretionary Symbols
price_table = pickle.load(open('sp500_price_table.pkl',
'rb')).head(2000)[['AAPL', 'SPY']]
daily_returns = price_table / price_table.shift(-1) - 1
model = sm.OLS(daily_returns['AAPL'], daily_returns['SPY'], missing='drop')
results = model.fit()
# Calculate Adjusted Returns
stock = 'SPY'
index = 'CL'
lookback = 2000
rolling_window = 10
scrub_params = ScrubParams(.1, .0125, .8)
beta_lookback = 2000
beta_object = Beta(stock, index, beta_lookback, scrub_params)
beta = beta_object.beta
beta = 0
adj_stock_line = StockLineBetaAdjusted(stock, lookback, beta, index)
adjusted_returns = adj_stock_line.adjusted_returns
adjusted_returns_scrubbed = adj_stock_line.adjusted_returns_scrubbed(.1)
adjusted_returns_scrubbed_rolling = adjusted_returns_scrubbed[::-1].rolling(
rolling_window).sum()
adjusted_returns_scrubbed_rolling = adjusted_returns_scrubbed[::-1].rolling(
1).sum()
print(adjusted_returns_scrubbed[::-1].head())
print(adjusted_returns_scrubbed_rolling.head())
# Calculate n-Day Rolling HVs.
rolling_HVs = adjusted_returns_scrubbed.iloc[::-1].rolling(
window=rolling_window).std() * math.sqrt(252)
#print(rolling_HVs[::-1].head(500))
def alpha_df(df: 'df of prices', lookback):
prices_df = df.head(lookback)
#returns_df = daily_returns(prices_df).dropna(axis=0, how='any')
returns_df = daily_returns(prices_df)
#print(prices_df.isnull().values.ravel().sum())
stocks = df.columns.values.tolist()
total_returns = []
HVs = []
alpha_ratios = []
adj_alpha_ratios = []
sample_sizes = []
betas = []
times = []
#correlations = []
#for stock in stocks:
# result = prices_df[prices_df[stock].isnull()].index.tolist()
# print(stock, ": ", result, sep='')
for stock in stocks:
prices_df_stock = prices_df[stock].dropna(axis=0, how='any').to_frame()
daily_returns_df_stock = daily_returns(prices_df_stock).dropna(
axis=0, how='any')
# Calculate Adjusted Returns
index = 'IBB'
if stock == 'SPY':
index = 'IWM'
beta_object = Beta(stock, index, 500, ScrubParams(.075, .0125, .8))
beta = beta_object.beta_value
#beta_value = 0
if stock == 'SPY':
beta = 0
adj_stock_line = StockLineBetaAdjusted(stock, lookback, beta, index)
prices_df_stock = adj_stock_line.prices_df
daily_returns_df_stock = adj_stock_line.adjusted_returns
num_observations = len(daily_returns_df_stock.values.tolist())
#total_return = prices_df_stock.head(1).iloc[0] / prices_df_stock.tail(1).iloc[0] - 1
#print('HEREEE', total_move)
#total_return = get_total_return(prices_df_stock)
total_return = adj_stock_line.total_return
total_returns.append(total_return)
HV = calc_HV(daily_returns_df_stock)
HVs.append(HV)
sample_size = get_sample_size(daily_returns_df_stock)
sample_sizes.append(sample_size)
alpha_ratio = total_return / HV
alpha_ratios.append(alpha_ratio)
# Should the Time Adjustment be sqrt(time)?
adj_alpha_ratio = alpha_ratio * math.sqrt(252 / num_observations)
adj_alpha_ratio = alpha_ratio * (252 / num_observations)
std_dev_over_time_period = HV * math.sqrt(num_observations / 252)
adj_alpha_ratio = total_return / std_dev_over_time_period
adj_alpha_ratios.append(adj_alpha_ratio)
betas.append(beta)
#correlation = beta_object.corr
#correlations.append(correlation)
time = num_observations / 252
times.append(time)
alpha_df_info = {
'Stock': stocks,
'Total_Return': total_returns,
'HV': HVs,
'Alpha_Ratio': alpha_ratios,
'Adj_Alpha_Ratio': adj_alpha_ratios,
'Sample_Size': sample_sizes,
'Beta': betas,
#'Correlation': correlations,
'Time': times
}
alpha_df = pd.DataFrame(alpha_df_info).set_index('Stock').loc[:, [
'Total_Return', 'HV', 'Alpha_Ratio', 'Adj_Alpha_Ratio', 'Sample_Size',
'Beta', 'Time'
]].round(3)
return alpha_df