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edhec_risk_kit_128.py
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edhec_risk_kit_128.py
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import pandas as pd
import numpy as np
import math
def get_ffme_returns():
"""
Load the Fama-French Dataset for the returns of the Top and Bottom Deciles by MarketCap
"""
me_m = pd.read_csv("data/Portfolios_Formed_on_ME_monthly_EW.csv",
header=0, index_col=0, na_values=-99.99)
rets = me_m[['Lo 10', 'Hi 10']]
rets.columns = ['SmallCap', 'LargeCap']
rets = rets/100
rets.index = pd.to_datetime(rets.index, format="%Y%m").to_period('M')
return rets
def get_hfi_returns():
"""
Load and format the EDHEC Hedge Fund Index Returns
"""
hfi = pd.read_csv("data/edhec-hedgefundindices.csv",
header=0, index_col=0, parse_dates=True)
hfi = hfi/100
hfi.index = hfi.index.to_period('M')
return hfi
def get_ind_file(filetype):
"""
Load and format the Ken French 30 Industry Portfolios files
"""
known_types = ["returns", "nfirms", "size"]
if filetype not in known_types:
sep = ','
raise ValueError(f'filetype must be one of:{sep.join(known_types)}')
if filetype is "returns":
name = "vw_rets"
divisor = 100
elif filetype is "nfirms":
name = "nfirms"
divisor = 1
elif filetype is "size":
name = "size"
divisor = 1
ind = pd.read_csv(f"data/ind30_m_{name}.csv", header=0, index_col=0)/divisor
ind.index = pd.to_datetime(ind.index, format="%Y%m").to_period('M')
ind.columns = ind.columns.str.strip()
return ind
def get_ind_returns():
"""
Load and format the Ken French 30 Industry Portfolios Value Weighted Monthly Returns
"""
return get_ind_file("returns")
def get_ind_nfirms():
"""
Load and format the Ken French 30 Industry Portfolios Average number of Firms
"""
return get_ind_file("nfirms")
def get_ind_size():
"""
Load and format the Ken French 30 Industry Portfolios Average size (market cap)
"""
return get_ind_file("size")
def get_total_market_index_returns():
"""
Load the 30 industry portfolio data and derive the returns of a capweighted total market index
"""
ind_nfirms = get_ind_nfirms()
ind_size = get_ind_size()
ind_return = get_ind_returns()
ind_mktcap = ind_nfirms * ind_size
total_mktcap = ind_mktcap.sum(axis=1)
ind_capweight = ind_mktcap.divide(total_mktcap, axis="rows")
total_market_return = (ind_capweight * ind_return).sum(axis="columns")
return total_market_return
def skewness(r):
"""
Alternative to scipy.stats.skew()
Computes the skewness of the supplied Series or DataFrame
Returns a float or a Series
"""
demeaned_r = r - r.mean()
# use the population standard deviation, so set dof=0
sigma_r = r.std(ddof=0)
exp = (demeaned_r**3).mean()
return exp/sigma_r**3
def kurtosis(r):
"""
Alternative to scipy.stats.kurtosis()
Computes the kurtosis of the supplied Series or DataFrame
Returns a float or a Series
"""
demeaned_r = r - r.mean()
# use the population standard deviation, so set dof=0
sigma_r = r.std(ddof=0)
exp = (demeaned_r**4).mean()
return exp/sigma_r**4
def compound(r):
"""
returns the result of compounding the set of returns in r
"""
return np.expm1(np.log1p(r).sum())
def annualize_rets(r, periods_per_year):
"""
Annualizes a set of returns
We should infer the periods per year
but that is currently left as an exercise
to the reader :-)
"""
compounded_growth = (1+r).prod()
n_periods = r.shape[0]
return compounded_growth**(periods_per_year/n_periods)-1
def annualize_vol(r, periods_per_year):
"""
Annualizes the vol of a set of returns
We should infer the periods per year
but that is currently left as an exercise
to the reader :-)
"""
return r.std()*(periods_per_year**0.5)
def sharpe_ratio(r, riskfree_rate, periods_per_year):
"""
Computes the annualized sharpe ratio of a set of returns
"""
# convert the annual riskfree rate to per period
rf_per_period = (1+riskfree_rate)**(1/periods_per_year)-1
excess_ret = r - rf_per_period
ann_ex_ret = annualize_rets(excess_ret, periods_per_year)
ann_vol = annualize_vol(r, periods_per_year)
return ann_ex_ret/ann_vol
import scipy.stats
def is_normal(r, level=0.01):
"""
Applies the Jarque-Bera test to determine if a Series is normal or not
Test is applied at the 1% level by default
Returns True if the hypothesis of normality is accepted, False otherwise
"""
if isinstance(r, pd.DataFrame):
return r.aggregate(is_normal)
else:
statistic, p_value = scipy.stats.jarque_bera(r)
return p_value > level
def drawdown(return_series: pd.Series):
"""Takes a time series of asset returns.
returns a DataFrame with columns for
the wealth index,
the previous peaks, and
the percentage drawdown
"""
wealth_index = 1000*(1+return_series).cumprod()
previous_peaks = wealth_index.cummax()
drawdowns = (wealth_index - previous_peaks)/previous_peaks
return pd.DataFrame({"Wealth": wealth_index,
"Previous Peak": previous_peaks,
"Drawdown": drawdowns})
def semideviation(r):
"""
Returns the semideviation aka negative semideviation of r
r must be a Series or a DataFrame, else raises a TypeError
"""
if isinstance(r, pd.Series):
is_negative = r < 0
return r[is_negative].std(ddof=0)
elif isinstance(r, pd.DataFrame):
return r.aggregate(semideviation)
else:
raise TypeError("Expected r to be a Series or DataFrame")
def var_historic(r, level=5):
"""
Returns the historic Value at Risk at a specified level
i.e. returns the number such that "level" percent of the returns
fall below that number, and the (100-level) percent are above
"""
if isinstance(r, pd.DataFrame):
return r.aggregate(var_historic, level=level)
elif isinstance(r, pd.Series):
return -np.percentile(r, level)
else:
raise TypeError("Expected r to be a Series or DataFrame")
def cvar_historic(r, level=5):
"""
Computes the Conditional VaR of Series or DataFrame
"""
if isinstance(r, pd.Series):
is_beyond = r <= -var_historic(r, level=level)
return -r[is_beyond].mean()
elif isinstance(r, pd.DataFrame):
return r.aggregate(cvar_historic, level=level)
else:
raise TypeError("Expected r to be a Series or DataFrame")
from scipy.stats import norm
def var_gaussian(r, level=5, modified=False):
"""
Returns the Parametric Gauusian VaR of a Series or DataFrame
If "modified" is True, then the modified VaR is returned,
using the Cornish-Fisher modification
"""
# compute the Z score assuming it was Gaussian
z = norm.ppf(level/100)
if modified:
# modify the Z score based on observed skewness and kurtosis
s = skewness(r)
k = kurtosis(r)
z = (z +
(z**2 - 1)*s/6 +
(z**3 -3*z)*(k-3)/24 -
(2*z**3 - 5*z)*(s**2)/36
)
return -(r.mean() + z*r.std(ddof=0))
def portfolio_return(weights, returns):
"""
Computes the return on a portfolio from constituent returns and weights
weights are a numpy array or Nx1 matrix and returns are a numpy array or Nx1 matrix
"""
return weights.T @ returns
def portfolio_vol(weights, covmat):
"""
Computes the vol of a portfolio from a covariance matrix and constituent weights
weights are a numpy array or N x 1 maxtrix and covmat is an N x N matrix
"""
return (weights.T @ covmat @ weights)**0.5
def plot_ef2(n_points, er, cov):
"""
Plots the 2-asset efficient frontier
"""
if er.shape[0] != 2 or er.shape[0] != 2:
raise ValueError("plot_ef2 can only plot 2-asset frontiers")
weights = [np.array([w, 1-w]) for w in np.linspace(0, 1, n_points)]
rets = [portfolio_return(w, er) for w in weights]
vols = [portfolio_vol(w, cov) for w in weights]
ef = pd.DataFrame({
"Returns": rets,
"Volatility": vols
})
return ef.plot.line(x="Volatility", y="Returns", style=".-")
from scipy.optimize import minimize
def minimize_vol(target_return, er, cov):
"""
Returns the optimal weights that achieve the target return
given a set of expected returns and a covariance matrix
"""
n = er.shape[0]
init_guess = np.repeat(1/n, n)
bounds = ((0.0, 1.0),) * n # an N-tuple of 2-tuples!
# construct the constraints
weights_sum_to_1 = {'type': 'eq',
'fun': lambda weights: np.sum(weights) - 1
}
return_is_target = {'type': 'eq',
'args': (er,),
'fun': lambda weights, er: target_return - portfolio_return(weights,er)
}
weights = minimize(portfolio_vol, init_guess,
args=(cov,), method='SLSQP',
options={'disp': False},
constraints=(weights_sum_to_1,return_is_target),
bounds=bounds)
return weights.x
def msr(riskfree_rate, er, cov):
"""
Returns the weights of the portfolio that gives you the maximum sharpe ratio
given the riskfree rate and expected returns and a covariance matrix
"""
n = er.shape[0]
init_guess = np.repeat(1/n, n)
bounds = ((0.0, 1.0),) * n # an N-tuple of 2-tuples!
# construct the constraints
weights_sum_to_1 = {'type': 'eq',
'fun': lambda weights: np.sum(weights) - 1
}
def neg_sharpe(weights, riskfree_rate, er, cov):
"""
Returns the negative of the sharpe ratio
of the given portfolio
"""
r = portfolio_return(weights, er)
vol = portfolio_vol(weights, cov)
return -(r - riskfree_rate)/vol
weights = minimize(neg_sharpe, init_guess,
args=(riskfree_rate, er, cov), method='SLSQP',
options={'disp': False},
constraints=(weights_sum_to_1,),
bounds=bounds)
return weights.x
def gmv(cov):
"""
Returns the weights of the Global Minimum Volatility portfolio
given a covariance matrix
"""
n = cov.shape[0]
return msr(0, np.repeat(1, n), cov)
def optimal_weights(n_points, er, cov):
"""
Returns a list of weights that represent a grid of n_points on the efficient frontier
"""
target_rs = np.linspace(er.min(), er.max(), n_points)
weights = [minimize_vol(target_return, er, cov) for target_return in target_rs]
return weights
def plot_ef(n_points, er, cov, style='.-', legend=False, show_cml=False, riskfree_rate=0, show_ew=False, show_gmv=False):
"""
Plots the multi-asset efficient frontier
"""
weights = optimal_weights(n_points, er, cov)
rets = [portfolio_return(w, er) for w in weights]
vols = [portfolio_vol(w, cov) for w in weights]
ef = pd.DataFrame({
"Returns": rets,
"Volatility": vols
})
ax = ef.plot.line(x="Volatility", y="Returns", style=style, legend=legend)
if show_cml:
ax.set_xlim(left = 0)
# get MSR
w_msr = msr(riskfree_rate, er, cov)
r_msr = portfolio_return(w_msr, er)
vol_msr = portfolio_vol(w_msr, cov)
# add CML
cml_x = [0, vol_msr]
cml_y = [riskfree_rate, r_msr]
ax.plot(cml_x, cml_y, color='green', marker='o', linestyle='dashed', linewidth=2, markersize=10)
if show_ew:
n = er.shape[0]
w_ew = np.repeat(1/n, n)
r_ew = portfolio_return(w_ew, er)
vol_ew = portfolio_vol(w_ew, cov)
# add EW
ax.plot([vol_ew], [r_ew], color='goldenrod', marker='o', markersize=10)
if show_gmv:
w_gmv = gmv(cov)
r_gmv = portfolio_return(w_gmv, er)
vol_gmv = portfolio_vol(w_gmv, cov)
# add EW
ax.plot([vol_gmv], [r_gmv], color='midnightblue', marker='o', markersize=10)
return ax
def run_cppi(risky_r, safe_r=None, m=3, start=1000, floor=0.8, riskfree_rate=0.03, drawdown=None):
"""
Run a backtest of the CPPI strategy, given a set of returns for the risky asset
Returns a dictionary containing: Asset Value History, Risk Budget History, Risky Weight History
"""
# set up the CPPI parameters
dates = risky_r.index
n_steps = len(dates)
account_value = start
floor_value = start*floor
peak = account_value
if isinstance(risky_r, pd.Series):
risky_r = pd.DataFrame(risky_r, columns=["R"])
if safe_r is None:
safe_r = pd.DataFrame().reindex_like(risky_r)
safe_r.values[:] = riskfree_rate/12 # fast way to set all values to a number
# set up some DataFrames for saving intermediate values
account_history = pd.DataFrame().reindex_like(risky_r)
risky_w_history = pd.DataFrame().reindex_like(risky_r)
cushion_history = pd.DataFrame().reindex_like(risky_r)
floorval_history = pd.DataFrame().reindex_like(risky_r)
peak_history = pd.DataFrame().reindex_like(risky_r)
for step in range(n_steps):
if drawdown is not None:
peak = np.maximum(peak, account_value)
floor_value = peak*(1-drawdown)
cushion = (account_value - floor_value)/account_value
risky_w = m*cushion
risky_w = np.minimum(risky_w, 1)
risky_w = np.maximum(risky_w, 0)
safe_w = 1-risky_w
risky_alloc = account_value*risky_w
safe_alloc = account_value*safe_w
# recompute the new account value at the end of this step
account_value = risky_alloc*(1+risky_r.iloc[step]) + safe_alloc*(1+safe_r.iloc[step])
# save the histories for analysis and plotting
cushion_history.iloc[step] = cushion
risky_w_history.iloc[step] = risky_w
account_history.iloc[step] = account_value
floorval_history.iloc[step] = floor_value
peak_history.iloc[step] = peak
risky_wealth = start*(1+risky_r).cumprod()
backtest_result = {
"Wealth": account_history,
"Risky Wealth": risky_wealth,
"Risk Budget": cushion_history,
"Risky Allocation": risky_w_history,
"m": m,
"start": start,
"floor": floor,
"risky_r":risky_r,
"safe_r": safe_r,
"drawdown": drawdown,
"peak": peak_history,
"floor": floorval_history
}
return backtest_result
def summary_stats(r, riskfree_rate=0.03):
"""
Return a DataFrame that contains aggregated summary stats for the returns in the columns of r
"""
ann_r = r.aggregate(annualize_rets, periods_per_year=12)
ann_vol = r.aggregate(annualize_vol, periods_per_year=12)
ann_sr = r.aggregate(sharpe_ratio, riskfree_rate=riskfree_rate, periods_per_year=12)
dd = r.aggregate(lambda r: drawdown(r).Drawdown.min())
skew = r.aggregate(skewness)
kurt = r.aggregate(kurtosis)
cf_var5 = r.aggregate(var_gaussian, modified=True)
hist_cvar5 = r.aggregate(cvar_historic)
return pd.DataFrame({
"Annualized Return": ann_r,
"Annualized Vol": ann_vol,
"Skewness": skew,
"Kurtosis": kurt,
"Cornish-Fisher VaR (5%)": cf_var5,
"Historic CVaR (5%)": hist_cvar5,
"Sharpe Ratio": ann_sr,
"Max Drawdown": dd
})
def gbm(n_years = 10, n_scenarios=1000, mu=0.07, sigma=0.15, steps_per_year=12, s_0=100.0, prices=True):
"""
Evolution of Geometric Brownian Motion trajectories, such as for Stock Prices through Monte Carlo
:param n_years: The number of years to generate data for
:param n_paths: The number of scenarios/trajectories
:param mu: Annualized Drift, e.g. Market Return
:param sigma: Annualized Volatility
:param steps_per_year: granularity of the simulation
:param s_0: initial value
:return: a numpy array of n_paths columns and n_years*steps_per_year rows
"""
# Derive per-step Model Parameters from User Specifications
dt = 1/steps_per_year
n_steps = int(n_years*steps_per_year) + 1
# the standard way ...
# rets_plus_1 = np.random.normal(loc=mu*dt+1, scale=sigma*np.sqrt(dt), size=(n_steps, n_scenarios))
# without discretization error ...
rets_plus_1 = np.random.normal(loc=(1+mu)**dt, scale=(sigma*np.sqrt(dt)), size=(n_steps, n_scenarios))
rets_plus_1[0] = 1
ret_val = s_0*pd.DataFrame(rets_plus_1).cumprod() if prices else rets_plus_1-1
return ret_val
def discount(t, r):
"""
Compute the price of a pure discount bond that pays a dollar at time period t
and r is the per-period interest rate
returns a |t| x |r| Series or DataFrame
r can be a float, Series or DataFrame
returns a DataFrame indexed by t
"""
discounts = pd.DataFrame([(r+1)**-i for i in t])
discounts.index = t
return discounts
def pv(flows, r):
"""
Compute the present value of a sequence of cash flows given by the time (as an index) and amounts
r can be a scalar, or a Series or DataFrame with the number of rows matching the num of rows in flows
"""
dates = flows.index
discounts = discount(dates, r)
return discounts.multiply(flows, axis='rows').sum()
def funding_ratio(assets, liabilities, r):
"""
Computes the funding ratio of a series of liabilities, based on an interest rate and current value of assets
"""
return pv(assets, r)/pv(liabilities, r)
def inst_to_ann(r):
"""
Convert an instantaneous interest rate to an annual interest rate
"""
return np.expm1(r)
def ann_to_inst(r):
"""
Convert an instantaneous interest rate to an annual interest rate
"""
return np.log1p(r)
def cir(n_years = 10, n_scenarios=1, a=0.05, b=0.03, sigma=0.05, steps_per_year=12, r_0=None):
"""
Generate random interest rate evolution over time using the CIR model
b and r_0 are assumed to be the annualized rates, not the short rate
and the returned values are the annualized rates as well
"""
if r_0 is None: r_0 = b
r_0 = ann_to_inst(r_0)
dt = 1/steps_per_year
num_steps = int(n_years*steps_per_year) + 1 # because n_years might be a float
shock = np.random.normal(0, scale=np.sqrt(dt), size=(num_steps, n_scenarios))
rates = np.empty_like(shock)
rates[0] = r_0
## For Price Generation
h = math.sqrt(a**2 + 2*sigma**2)
prices = np.empty_like(shock)
####
def price(ttm, r):
_A = ((2*h*math.exp((h+a)*ttm/2))/(2*h+(h+a)*(math.exp(h*ttm)-1)))**(2*a*b/sigma**2)
_B = (2*(math.exp(h*ttm)-1))/(2*h + (h+a)*(math.exp(h*ttm)-1))
_P = _A*np.exp(-_B*r)
return _P
prices[0] = price(n_years, r_0)
####
for step in range(1, num_steps):
r_t = rates[step-1]
d_r_t = a*(b-r_t)*dt + sigma*np.sqrt(r_t)*shock[step]
rates[step] = abs(r_t + d_r_t)
# generate prices at time t as well ...
prices[step] = price(n_years-step*dt, rates[step])
rates = pd.DataFrame(data=inst_to_ann(rates), index=range(num_steps))
### for prices
prices = pd.DataFrame(data=prices, index=range(num_steps))
###
return rates, prices
def bond_cash_flows(maturity, principal=100, coupon_rate=0.03, coupons_per_year=12):
"""
Returns the series of cash flows generated by a bond,
indexed by the payment/coupon number
"""
n_coupons = round(maturity*coupons_per_year)
coupon_amt = principal*coupon_rate/coupons_per_year
coupons = np.repeat(coupon_amt, n_coupons)
coupon_times = np.arange(1, n_coupons+1)
cash_flows = pd.Series(data=coupon_amt, index=coupon_times)
cash_flows.iloc[-1] += principal
return cash_flows
def bond_price(maturity, principal=100, coupon_rate=0.03, coupons_per_year=12, discount_rate=0.03):
"""
Computes the price of a bond that pays regular coupons until maturity
at which time the principal and the final coupon is returned
This is not designed to be efficient, rather,
it is to illustrate the underlying principle behind bond pricing!
If discount_rate is a DataFrame, then this is assumed to be the rate on each coupon date
and the bond value is computed over time.
i.e. The index of the discount_rate DataFrame is assumed to be the coupon number
"""
if isinstance(discount_rate, pd.DataFrame):
pricing_dates = discount_rate.index
prices = pd.DataFrame(index=pricing_dates, columns=discount_rate.columns)
for t in pricing_dates:
prices.loc[t] = bond_price(maturity-t/coupons_per_year, principal, coupon_rate, coupons_per_year,
discount_rate.loc[t])
return prices
else: # base case ... single time period
if maturity <= 0: return principal+principal*coupon_rate/coupons_per_year
cash_flows = bond_cash_flows(maturity, principal, coupon_rate, coupons_per_year)
return pv(cash_flows, discount_rate/coupons_per_year)
def macaulay_duration(flows, discount_rate):
"""
Computes the Macaulay Duration of a sequence of cash flows, given a per-period discount rate
"""
discounted_flows = discount(flows.index, discount_rate)*pd.DataFrame(flows)
weights = discounted_flows/discounted_flows.sum()
return np.average(flows.index, weights=weights.iloc[:,0])
def match_durations(cf_t, cf_s, cf_l, discount_rate):
"""
Returns the weight W in cf_s that, along with (1-W) in cf_l will have an effective
duration that matches cf_t
"""
d_t = macaulay_duration(cf_t, discount_rate)
d_s = macaulay_duration(cf_s, discount_rate)
d_l = macaulay_duration(cf_l, discount_rate)
return (d_l - d_t)/(d_l - d_s)
def bond_total_return(monthly_prices, principal, coupon_rate, coupons_per_year):
"""
Computes the total return of a Bond based on monthly bond prices and coupon payments
Assumes that dividends (coupons) are paid out at the end of the period (e.g. end of 3 months for quarterly div)
and that dividends are reinvested in the bond
"""
coupons = pd.DataFrame(data = 0, index=monthly_prices.index, columns=monthly_prices.columns)
t_max = monthly_prices.index.max()
pay_date = np.linspace(12/coupons_per_year, t_max, int(coupons_per_year*t_max/12), dtype=int)
coupons.iloc[pay_date] = principal*coupon_rate/coupons_per_year
total_returns = (monthly_prices + coupons)/monthly_prices.shift()-1
return total_returns.dropna()
def bt_mix(r1, r2, allocator, **kwargs):
"""
Runs a back test (simulation) of allocating between a two sets of returns
r1 and r2 are T x N DataFrames or returns where T is the time step index and N is the number of scenarios.
allocator is a function that takes two sets of returns and allocator specific parameters, and produces
an allocation to the first portfolio (the rest of the money is invested in the GHP) as a T x 1 DataFrame
Returns a T x N DataFrame of the resulting N portfolio scenarios
"""
if not r1.shape == r2.shape:
raise ValueError("r1 and r2 should have the same shape")
weights = allocator(r1, r2, **kwargs)
if not weights.shape == r1.shape:
raise ValueError("Allocator returned weights with a different shape than the returns")
r_mix = weights*r1 + (1-weights)*r2
return r_mix
def fixedmix_allocator(r1, r2, w1, **kwargs):
"""
Produces a time series over T steps of allocations between the PSP and GHP across N scenarios
PSP and GHP are T x N DataFrames that represent the returns of the PSP and GHP such that:
each column is a scenario
each row is the price for a timestep
Returns an T x N DataFrame of PSP Weights
"""
return pd.DataFrame(data = w1, index=r1.index, columns=r1.columns)
def terminal_values(rets):
"""
Computes the terminal values from a set of returns supplied as a T x N DataFrame
Return a Series of length N indexed by the columns of rets
"""
return (rets+1).prod()
def terminal_stats(rets, floor = 0.8, cap=np.inf, name="Stats"):
"""
Produce Summary Statistics on the terminal values per invested dollar
across a range of N scenarios
rets is a T x N DataFrame of returns, where T is the time-step (we assume rets is sorted by time)
Returns a 1 column DataFrame of Summary Stats indexed by the stat name
"""
terminal_wealth = (rets+1).prod()
breach = terminal_wealth < floor
reach = terminal_wealth >= cap
p_breach = breach.mean() if breach.sum() > 0 else np.nan
p_reach = reach.mean() if reach.sum() > 0 else np.nan
e_short = (floor-terminal_wealth[breach]).mean() if breach.sum() > 0 else np.nan
e_surplus = (-cap+terminal_wealth[reach]).mean() if reach.sum() > 0 else np.nan
sum_stats = pd.DataFrame.from_dict({
"mean": terminal_wealth.mean(),
"std" : terminal_wealth.std(),
"p_breach": p_breach,
"e_short":e_short,
"p_reach": p_reach,
"e_surplus": e_surplus
}, orient="index", columns=[name])
return sum_stats
def glidepath_allocator(r1, r2, start_glide=1, end_glide=0.0):
"""
Allocates weights to r1 starting at start_glide and ends at end_glide
by gradually moving from start_glide to end_glide over time
"""
n_points = r1.shape[0]
n_col = r1.shape[1]
path = pd.Series(data=np.linspace(start_glide, end_glide, num=n_points))
paths = pd.concat([path]*n_col, axis=1)
paths.index = r1.index
paths.columns = r1.columns
return paths