def market_neutral_sharpe(ticker, benchmark='SPY', start='2017-01-01'):
tickers = pd.DataFrame(make_portfolio(ticker, start=start))
benchs = pd.DataFrame(make_portfolio(benchmark, start=start))
tickers['daily_ret'] = tickers['adjClose'].pct_change()
benchs['daily_ret'] = benchs['adjClose'].pct_change()
#print(tickers)
df = pd.DataFrame(index=tickers.index)
df['net'] = (tickers['daily_ret'] - benchs['daily_ret']) / 2.0
return annual_sharpe(df['net'])
def MonteCarloSimulation(stock, simulations=500):
stocks = make_portfolio(stock) # download financial data of stocks
returns = stocks.pct_change() #pct change of stock - daily returns
num_sim = simulations
last_price = stocks[-1]
trading_dates = 253
sim_df = pd.DataFrame()
for x in range(num_sim):
count = 0
daily_STD = returns.std()
price_series = []
price = last_price * (1 + np.random.normal(0, daily_STD))
price_series.append(price)
for y in range(trading_dates):
if count == 253:
break
price = price_series[count] * (1 + np.random.normal(0, daily_STD))
price_series.append(price)
count += 1
sim_df[x] = price_series
#print(sim_df)
f, ax1 = plt.subplots(1, 1, figsize=(15, 5))
ax1.plot(sim_df)
ax1.axhline(y=last_price, color='r', linestyle='-', linewidth=2)
#ax2.hist(sim_df,bins=100)
rnd = np.mean(sim_df)
q5 = np.percentile(sim_df, 5)
q95 = np.percentile(sim_df, 95)
print(
f'There is a 5% chance that our stock price will end up below around {q5} and a 5% chance it will finish above {q95} ',
)
plt.show()
def sharp(ticker, rf=0.017):
pdf = make_portfolio(ticker)
pdf = pd.DataFrame(pdf)
pdf['daily_ret'] = pdf['adjClose'].pct_change() * 100
sharp = pdf['daily_ret'].mean() / pdf['daily_ret'].std()
annual_sh = np.sqrt(253) * sharp
print(annual_sh)
def equity_sharpe(ticker, rf=0.0173):
"""
Calculates the annualised Sharpe ratio based on the daily
returns of an equity
"""
pdf = make_portfolio(ticker)
pdf = pd.DataFrame(pdf)
pdf['daily_ret'] = pdf['adjClose'].pct_change()
pdf['excess_daily_ret'] = pdf['daily_ret'] - rf / 252
return annual_sharpe(pdf['excess_daily_ret'])
def CAPM(ticker, benchmark, start='2016-09-27'):
import statsmodels.regression.linear_model as lm
import statsmodels.tools.tools as ct
df = make_portfolio([ticker, benchmark], start=start)
ticker_df = df[ticker].pct_change()[1:]
bench_df = df[benchmark].pct_change()[1:]
const = ct.add_constant(bench_df)
capm = lm.OLS(ticker_df, const)
results = capm.fit()
return results.summary()
def beta(ticker, benchmark, start='2016-09-27'):
'''A stock's beta coefficient is a measure of its volatility over time compared to a market benchmark.
A beta of 1 means that a stock's volatility matches up exactly with the markets.
A higher beta indicates great volatility, and a lower beta indicates less volatility.
:param ticker: ticker of our stocks
:param benchmark: benchmark - for most of big USA stocks we will consider SPY or DOW
'''
# we use our function to download stock data from tiingo API
df = make_portfolio([ticker, benchmark], start=start)
# we calculate pct changes for our ticker (x[0] - x[-1])/x[-1]
ticker_df = df[ticker].pct_change()[1:]
# we calculate pct changes for our benchmark (x[0] - x[-1])/x[-1]
bench_df = df[benchmark].pct_change()[1:]
# Compare how the stock and the index move relative to each other with a covariance formula
covariance_matrix = np.cov([ticker_df, bench_df])
cov = covariance_matrix[0, 1] # covariance of stocks and index
var = covariance_matrix[1, 1] # the variance of the index alone.
# Beta = Covariance of stocks and index / variance of index
beta = cov / var
alpha = np.mean(ticker_df) - beta * np.mean(bench_df)
return round(float(beta), 4)
def capm(ticker, benchmark, start='2016-09-27'):
'''Beta (ang. beta, risk factor) – współczynnik statystyczny,
ustalający stopień korelacji pomiędzy zwrotem z inwestycji w akcje danej spółki,
a hipotetycznej inwestycji w indeks rynku lub z określonego pakietu akcji różnych spółek.'''
df = make_portfolio([ticker, benchmark], start=start)
ticker_df = df[ticker].pct_change()[1:]
bench_df = df[benchmark].pct_change()[1:]
beta, intercept, r_value, p_value, std_err = stats.linregress(
bench_df, ticker_df)
#print(beta,intercept,r_value**2,p_value,std_err)
bet = round(float(beta), 4)
r2 = round(float(r_value**2), 4)
f, ax = plt.subplots(1, figsize=(15, 10))
ax.scatter(ticker_df, bench_df, label='Data', c='blue')
b, a = np.polyfit(bench_df, ticker_df, deg=1)
ax.plot(bench_df, b * bench_df + a, color='r', label='Capital Market Line')
plt.title('Capital Asset Pricing Model')
plt.xlabel('Market Returns ')
plt.ylabel('Stock Returns')
plt.text(0.045, 0.045, r'$R_i = \beta * R_m + \alpha$', fontsize=14)
plt.text(-0.08, 0.06, f'R Squared = {r2}\nBeta = {bet}', fontsize=14)
plt.show()