def adjusted_returns(self):
index_prices = PriceTable.head(self.lookback)[self.index]
index_returns = daily_returns(index_prices).to_frame()
df = (1 + self.daily_returns[self.stock]) / (
1 + index_returns[self.index] * self.beta) - 1
df.name = 'Adj. Returns'
return df
def __init__(self, stock: 'str', lookback: 'int'):
self.stock = stock
self.lookback = lookback
self.price_table = PriceTable.head(self.lookback)[[self.stock]]
self.daily_returns = daily_returns(self.price_table).head(
self.lookback)
def adjusted_returns(self):
index_prices = PriceTable.head(self.lookback)[self.index]
index_returns = daily_returns(index_prices).to_frame()
adj_returns_df = (1 + self.daily_returns[self.stock]) / (
1 + index_returns[self.index] * self.beta) - 1
adj_returns_df.name = self.adjusted_returns_df_column_name
adj_returns_df = adj_returns_df.to_frame()
return adj_returns_df
def __init__(self, stock: 'str', index: 'str', lookback: 'int', ScrubParams: 'obj'):
self.stock = stock
self.index = index
self.lookback = lookback
self.ScrubParams = ScrubParams
self.price_table = PriceTable.head(self.lookback)[[self.stock, self.index]]
self.daily_returns = daily_returns(self.price_table)
def __init__(self, stock: 'str', index: 'str', lookback: 'int', ScrubParams: 'obj'):
self.stock = stock
self.index = index
self.lookback = lookback
self.ScrubParams = ScrubParams
#self.price_table = pickle.load(open('sp500_price_table.pkl', 'rb')).head(self.lookback)[[self.stock, self.index]]
self.price_table = My_Data.PriceTable.head(self.lookback)[[self.stock, self.index]]
#self.daily_returns = self.price_table / self.price_table.shift(-1) - 1
self.daily_returns = daily_returns(self.price_table)
def __init__(self, stock: 'str', lookback: 'int', base = None):
self.stock = stock
self.lookback = lookback
if base is None:
self.base = self.stock
else:
self.base = base
self.price_table = PriceTable.head(self.lookback)[[self.stock]]
self.daily_returns = daily_returns(self.price_table).head(self.lookback)
def __init__(self, stock: 'str', index: 'str', beta: 'float',
to_graph: 'str', base: 'str', lookback: 'int'):
self.stock = stock
self.index = index
self.beta = beta
self.to_graph = to_graph
self.base = base
self.lookback = lookback
self.price_table = PriceTable.head(
self.lookback)[[self.stock, self.index]]
self.daily_returns = daily_returns(self.price_table).head(
self.lookback)
self.daily_returns['adj_returns'] = (1 + self.daily_returns[
self.stock]) / (1 + self.daily_returns[self.index] * self.beta) - 1
print(self.daily_returns.head(5))
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()
#for stock in stocks:
# result = prices_df[prices_df[stock].isnull()].index.tolist()
# print(stock, ": ", result, sep='')
total_returns = []
HVs = []
alpha_ratios = []
adj_alpha_ratios = []
sample_sizes = []
for stock in stocks:
prices_df_stock = prices_df[stock].dropna(axis=0, how='any').to_frame()
#print(prices_df_stock.head(2))
daily_returns_df_stock = daily_returns(prices_df_stock).dropna(
axis=0, how='any')
# Calculate Adjusted Returns
index = 'SPY'
beta = Beta(stock, index, lookback, ScrubParams(.075, .0125,
.8)).beta_value
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
#total_return = prices_df_stock.head(1).iloc[0] / prices_df_stock.tail(1).iloc[0] - 1
#print('HEREEE', total_move)
#total_return = prices_df_stock.head(1).iloc[0, 0] / prices_df_stock.tail(1).iloc[0, 0] - 1
#total_returns.append(total_return)
total_return = get_total_return(prices_df_stock)
total_returns.append(total_return)
#HV = daily_returns_df_stock.dropna(axis=0, how='any').std(ddof=0)*math.sqrt(252)
#HVs.append(HV)
HV = calc_HV(daily_returns_df_stock)
HVs.append(HV)
sample_size = daily_returns_df_stock.shape[0]
sample_sizes.append(sample_size)
alpha_ratio = total_return / HV
alpha_ratios.append(alpha_ratio)
adj_alpha_ratio = total_return * math.sqrt(252 / lookback) / HV
adj_alpha_ratios.append(adj_alpha_ratio)
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
}
alpha_df = pd.DataFrame(alpha_df_info)
alpha_df = pd.DataFrame(
list(
zip(stocks, total_returns, HVs, alpha_ratios, adj_alpha_ratios,
sample_sizes))).round(3)
alpha_df.rename(index=str,
columns={
0: 'Stock',
1: 'Total_Return',
2: 'HV',
3: 'Alpha_Ratio',
4: 'Adj_Alpha_Ratio',
5: 'Sample_Size'
},
inplace=True)
alpha_df.set_index('Stock', inplace=True)
return alpha_df
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
def get_daily_returns(stock, lookback):
price_table = get_stock_prices(stock, lookback)
return daily_returns(price_table)
