def volatility(
self,
n,
freq=None,
which="close",
ann=True,
model="ln",
min_periods=1,
rolling="simple",
):
"""Return the annualized volatility series. N is the number of lookback periods.
:param n: int, number of lookback periods
:param freq: resample frequency or None
:param which: price series to use
:param ann: If True then annualize
:param model: {'ln', 'pct', 'bbg'}
ln - use logarithmic price changes
pct - use pct price changes
bbg - use logarithmic price changes but Bloomberg uses actual business days
:param rolling:{'simple', 'exp'}, if exp, use ewmstd. if simple, use rolling_std
:return:
"""
if model not in ("bbg", "ln", "pct"):
raise ValueError("model must be one of (bbg, ln, pct), not %s" %
model)
if rolling not in ("simple", "exp"):
raise ValueError("rolling must be one of (simple, exp), not %s" %
rolling)
px = self.frame[which]
px = px if not freq else px.resample(freq, how="last")
if model == "bbg" and periods_in_year(px) == 252:
# Bloomberg uses business days, so need to convert and reindex
orig = px.index
px = px.resample("B").ffill()
chg = np.log(px / px.shift(1))
chg[chg.index - orig] = np.nan
if rolling == "simple":
vol = pd.rolling_std(chg, n,
min_periods=min_periods).reindex(orig)
else:
vol = pd.ewmstd(chg, span=n, min_periods=n)
return vol if not ann else vol * np.sqrt(260)
else:
chg = px.pct_change() if model == "pct" else np.log(px /
px.shift(1))
if rolling == "simple":
vol = pd.rolling_std(chg, n, min_periods=min_periods)
else:
vol = pd.ewmstd(chg, span=n, min_periods=n)
return vol if not ann else vol * np.sqrt(periods_in_year(vol))
def zscore_ranked(div, library):
lookback = 5
markets = 3
data = pd.DataFrame()
for mkt in library.list_symbols():
try:
data[mkt] = library.read(mkt).data.Price
except:
print mkt
zscores = (data - pd.ewma(data, 20)) / pd.ewmstd(data, 20)
latest = zscores.tail(lookback)
zscore_ranked = latest.T.sort_values(
by=latest.T.columns[0]).dropna()[:markets]
zscore_ranked = zscore_ranked.append(
latest.T.sort_values(by=latest.T.columns[0]).dropna()[-markets:])
final_data = pd.DataFrame()
i = 1
for d in zscore_ranked.columns:
final_data['T+' + str(i)] = zscore_ranked[d]
i = i + 1
return serialize(final_data,
render_to=div,
kind="bar",
title="Noteable market moves",
output_type='json')
def robust_vol_calc(x,
days=35,
min_periods=10,
vol_abs_min=0.0000000001,
vol_floor=True,
floor_min_quant=0.05,
floor_min_periods=100,
floor_days=500):
# Standard deviation will be nan for first 10 non nan values
vol = pd.ewmstd(x, span=days, min_periods=min_periods)
vol[vol < vol_abs_min] = vol_abs_min
if vol_floor:
# Find the rolling 5% quantile point to set as a minimum
vol_min = pd.rolling_quantile(vol, floor_days, floor_min_quant,
floor_min_periods)
# set this to zero for the first value then propogate forward, ensures
# we always have a value
vol_min.set_value(vol_min.index[0], 0.0)
vol_min = vol_min.ffill()
# apply the vol floor
vol_with_min = pd.concat([vol, vol_min], axis=1)
vol_floored = vol_with_min.max(axis=1, skipna=False)
else:
vol_floored = vol
return vol_floored
def vol_estimator(x, using_exponent=True, min_periods=20, ew_lookback=250):
"""
Generic vol estimator used for optimisation, works on data frames, produces a single answer
:param x: data
:type x: Tx1 pd.DataFrame
:param using_exponent: Use exponential or normal vol (latter recommended for bootstrapping)
:type using_exponent: bool
:param min_periods: The minimum number of observations (*default* 10)
:type min_periods: int
:returns: pd.DataFrame -- volatility measure
"""
if using_exponent:
vol = pd.ewmstd(x, span=ew_lookback,
min_periods=min_periods).iloc[-1, :].values[0]
else:
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
vol = x.apply(apply_with_min_periods,
axis=0,
min_periods=min_periods,
my_func=np.nanstd)
stdev_list = list(vol)
return stdev_list
def vol_estimator(x, using_exponent=True, min_periods=20, ew_lookback=250):
"""
Generic vol estimator used for optimisation, works on data frames, produces a single answer
:param x: data
:type x: Tx1 pd.DataFrame
:param using_exponent: Use exponential or normal vol (latter recommended for bootstrapping)
:type using_exponent: bool
:param min_periods: The minimum number of observations (*default* 10)
:type min_periods: int
:returns: pd.DataFrame -- volatility measure
"""
if using_exponent:
vol = pd.ewmstd(x, span=ew_lookback, min_periods=min_periods).iloc[-1,:].values[0]
else:
vol=x.apply(apply_with_min_periods,axis=0,min_periods=min_periods, my_func=np.nanstd)
stdev_list=list(vol)
return stdev_list
def robust_vol_calc(x, days=35, min_periods=10, vol_abs_min=0.0000000001, vol_floor=True,
floor_min_quant=0.05, floor_min_periods=100,
floor_days=500):
# Standard deviation will be nan for first 10 non nan values
vol = pd.ewmstd(x, span=days, min_periods=min_periods)
vol[vol < vol_abs_min] = vol_abs_min
if vol_floor:
# Find the rolling 5% quantile point to set as a minimum
vol_min = pd.rolling_quantile(
vol, floor_days, floor_min_quant, floor_min_periods)
# set this to zero for the first value then propogate forward, ensures
# we always have a value
vol_min.set_value(vol_min.index[0], 0.0)
vol_min = vol_min.ffill()
# apply the vol floor
vol_with_min = pd.concat([vol, vol_min], axis=1)
vol_floored = vol_with_min.max(axis=1, skipna=False)
else:
vol_floored = vol
return vol_floored
def portfolio_return(asset_returns, cash_weights):
index_returns = asset_returns.cumsum().ffill().diff()
cash_align = cash_weights.reindex(asset_returns.index, method="ffill")
cash_align[np.isnan(index_returns)] = 0.0
cash_align[np.isnan(cash_align)] = 0.0
vols = pd.ewmstd(asset_returns, span=100, min_periods=1)
riskweights = pd.DataFrame(cash_align.values / vols.values,
index=vols.index)
riskweights.columns = asset_returns.columns
riskweights[np.isnan(riskweights)] = 0.0
def _rowfix(x):
if all([y == 0.0 for y in x]):
return x
sumx = sum(x)
return [y / sumx for y in x]
riskweights = riskweights.apply(_rowfix, axis=1)
portfolio_returns = asset_returns * riskweights
portfolio_returns[np.isnan(portfolio_returns)] = 0.0
portfolio_returns = portfolio_returns.sum(axis=1)
return portfolio_returns
def standardize(data):
log_return = np.log(data).diff()
std = pd.ewmstd(log_return, 10)
ewma = pd.ewma(log_return, 10)
data_standardized = 1 / (1 + np.exp((log_return - ewma) / std))
return data_standardized
def macro_factors(div, library):
factors = {
'Risk on': ['Russell 2000', 'DAX'],
'Quantitative Easing':
['Gold', 'German Bund', 'Gilts', 'US Treasuries 10 Yr'],
'Emerging Markets':
['Copper', 'MXN', 'BRL', 'Ibovespa', 'Taiwan (SIMEX)'],
'EU': ['DAX', 'FTSE 100', 'German Bund', 'Italian 10 year bonds'],
'Energies': ['Crude', 'Rotterdam Coal', 'Natural Gas'],
'Industrials':
['Copper', 'Rotterdam Coal', 'Crude', 'Shanghai Rebar']
}
factor_data = pd.DataFrame()
for f in factors.keys():
df = pd.DataFrame()
for m in factors[f]:
try:
df[m] = library.read(m).data.Price.replace(to_replace=0,
method='ffill')
except:
print m
factor_data[f] = df.resample(
rule='d', how='last').dropna(how='all').pct_change().mean(axis=1)
lookback = 5
zscores = (factor_data.cumsum() - pd.ewma(
factor_data.cumsum(), 60)) / pd.ewmstd(factor_data.cumsum(), 60)
y = zscores.tail(1).T.columns[0].year
return serialize(zscores[str(y)].ffill(),
render_to=div,
title='Factors',
output_type='json')
def robust_vol_calc(x,
days=35,
min_periods=10,
vol_abs_min=0.0000000001,
vol_floor=True,
floor_min_quant=0.05,
floor_min_periods=100,
floor_days=500):
"""
Robust exponential volatility calculation, assuming daily series of prices
We apply an absolute minimum level of vol (absmin);
and a volfloor based on lowest vol over recent history
:param x: data
:type x: Tx1 pd.Series
:param days: Number of days in lookback (*default* 35)
:type days: int
:param min_periods: The minimum number of observations (*default* 10)
:type min_periods: int
:param vol_abs_min: The size of absolute minimum (*default* =0.0000000001) 0.0= not used
:type absmin: float or None
:param vol_floor Apply a floor to volatility (*default* True)
:type vol_floor: bool
:param floor_min_quant: The quantile to use for volatility floor (eg 0.05 means we use 5% vol) (*default 0.05)
:type floor_min_quant: float
:param floor_days: The lookback for calculating volatility floor, in days (*default* 500)
:type floor_days: int
:param floor_min_periods: Minimum observations for floor - until reached floor is zero (*default* 100)
:type floor_min_periods: int
:returns: pd.DataFrame -- volatility measure
"""
# Standard deviation will be nan for first 10 non nan values
vol = pd.ewmstd(x, span=days, min_periods=min_periods)
vol[vol < vol_abs_min] = vol_abs_min
if vol_floor:
# Find the rolling 5% quantile point to set as a minimum
vol_min = pd.rolling_quantile(vol, floor_days, floor_min_quant,
floor_min_periods)
# set this to zero for the first value then propogate forward, ensures
# we always have a value
vol_min.set_value(vol_min.index[0], 0.0)
vol_min = vol_min.ffill()
# apply the vol floor
vol_with_min = pd.concat([vol, vol_min], axis=1)
vol_floored = vol_with_min.max(axis=1, skipna=False)
else:
vol_floored = vol
return vol_floored
def tsmom_improved(data, months):
vol = pd.ewmstd(data.pct_change(), 500) * math.sqrt(12)
data = data.resample(rule='m', how='last')
signal = data / data.shift(months) - 1
signal = signal / abs(signal)
position = signal / vol
return position
def expected_ewmstd(self, window_length, decay_rate):
alpha = 1 - decay_rate
span = (2 / alpha) - 1
return rolling_apply(
self.raw_data,
window_length,
lambda window: ewmstd(window, span=span)[-1],
)[window_length:]
def tsmom_daily(data, signal_lookback, vol_lookback=20):
mul = get_contract_multipliers()[data.columns]
vol = pd.ewmstd(data, vol_lookback,
min_periods=vol_lookback) * math.sqrt(256)
signal = pd.rolling_mean(data, signal_lookback)
signal = signal / abs(signal)
position = (signal / (vol * mul))
return position.shift(1)
def ts(df, panel):
hl = TS_HALFLIFE
min_per = 12
if panel:
hl = hl * df.index.levels[1].shape[0]
min_per = min_per * df.index.levels[1].shape[0]
m = pd.ewma(df, halflife=hl, min_periods=min_per)
std = pd.ewmstd(df, halflife=hl, min_periods=min_per)
return (df - m) / std
def calc_std(returns):
downside_only = False
if (downside_only):
returns = returns.copy()
returns[returns > 0.0] = np.nan
b = pd.ewmstd(
returns, halflife=20, adjust=True,
ignore_na=True).dropna() #halflife = 20 four week half life - mid-term
return b.iloc[-1]
def devol(self, _lambda=0.06, n_days=1):
_com = (1 - _lambda) / _lambda
self.df['LogReturns'] = np.log(
self.df.Close.pct_change(periods=n_days) + 1)
self.df['Vola'] = pd.ewmstd(self.df.LogReturns,
com=_com,
ignore_na=True)[2:]
self.df['DevolLogReturns'] = self.df.LogReturns / self.df.Vola
self.df.set_index('Date', inplace=True)
def trend(self,
tags,
top_n=None,
other=False,
resample='D',
cumulative=False,
ewmaspan=None):
""" show the supplied tags summed up per day """
if top_n is not None:
tags = self.top_n_tags(top_n, tags)
D = self.D[tags] if tags is not None else self.D
if other:
D['other'] = self.D[[t for t in self.D.keys()
if t not in tags]].sum(axis=1)
D = D.resample(resample, how='sum', label='left')
self._obfuscate(D)
D = D.fillna(0)
if ewmaspan is not None:
ewma = pd.ewma(D, span=ewmaspan)
ewmstd = pd.ewmstd(D, span=2 * ewmaspan)
if cumulative:
ewmstd = ewmstd * 3
ewma = ewma.cumsum()
if cumulative:
D = D.cumsum()
alpha = 0.5 if not cumulative and ewmaspan is not None else 1
ax = D.plot(linewidth=2,
colormap=self.cmapname,
legend=False,
alpha=alpha)
if ewmaspan is not None:
colors = self.cmap(np.linspace(0., 1., len(D.keys())))
if cumulative:
for idx, k in enumerate(tags):
ax.fill_between(D.index,
np.array(ewma[k] + ewmstd[k]).ravel(),
np.array(ewma[k] - ewmstd[k]).ravel(),
facecolor=colors[idx],
alpha=0.2,
linewidth=1)
ewma.plot(style='--',
legend=False,
ax=ax,
colormap=self.cmapname,
linewidth=2)
ax.legend(ax.lines[:len(D.keys())],
map(lambda x: x.get_label(), ax.lines[:len(D.keys())]),
loc='best')
ax.grid(True)
ax.set_ylim(0, D.max().max())
if cumulative:
plt.ylabel('Time Spent (h)')
else:
plt.ylabel('Time Spent (h) per Interval (%s)' % resample)
plt.xlabel('Interval ID')
def robust_vol_calc(x, days=35, min_periods=10, vol_abs_min=0.0000000001, vol_floor=True,
floor_min_quant=0.05, floor_min_periods=100,
floor_days=500):
"""
Robust exponential volatility calculation, assuming daily series of prices
We apply an absolute minimum level of vol (absmin);
and a volfloor based on lowest vol over recent history
:param x: data
:type x: Tx1 pd.DataFrame
:param days: Number of days in lookback (*default* 35)
:type days: int
:param min_periods: The minimum number of observations (*default* 10)
:type min_periods: int
:param vol_abs_min: The size of absolute minimum (*default* =0.0000000001) 0.0= not used
:type absmin: float or None
:param vol_floor Apply a floor to volatility (*default* True)
:type vol_floor: bool
:param floor_min_quant: The quantile to use for volatility floor (eg 0.05 means we use 5% vol) (*default 0.05)
:type floor_min_quant: float
:param floor_days: The lookback for calculating volatility floor, in days (*default* 500)
:type floor_days: int
:param floor_min_periods: Minimum observations for floor - until reached floor is zero (*default* 100)
:type floor_min_periods: int
:returns: pd.DataFrame -- volatility measure
"""
# Standard deviation will be nan for first 10 non nan values
vol = pd.ewmstd(x, span=days, min_periods=min_periods)
vol[vol < vol_abs_min] = vol_abs_min
if vol_floor:
# Find the rolling 5% quantile point to set as a minimum
vol_min = pd.rolling_quantile(
vol, floor_days, floor_min_quant, floor_min_periods)
# set this to zero for the first value then propogate forward, ensures
# we always have a value
vol_min.set_value(vol_min.index[0], vol_min.columns[0], 0.0)
vol_min = vol_min.ffill()
# apply the vol floor
vol_with_min = pd.concat([vol, vol_min], axis=1)
vol_floored = vol_with_min.max(axis=1, skipna=False).to_frame()
else:
vol_floored = vol
vol_floored.columns = ["vol"]
return vol_floored
def __init__( self,
prices,
volFunc = lambda returns: pd.ewmstd( returns, span = 252 ),
name = 'Empirical' ):
self.name = name
self.dates = prices.index
self.returns = prices.pct_change()
self.vol = volFunc( self.returns )
self.volDyad = dict( ( today, self.dyad( self.vol.ix[ today.date() ] ) ) for today in self.dates )
self.empCov = dict( ( today, self.returns[ 1:today.date() ].corr().as_matrix() * self.volDyad[ today ] )
for today in self.dates[1:] )
def ewma_mom_daily(data, short_lookback, long_lookback, vol_lookback=20):
mkts = data.columns
mul = get_contract_multipliers()[mkts]
vol = pd.ewmstd(data, vol_lookback,
min_periods=vol_lookback) * math.sqrt(256)
signal = signal = pd.ewma(data, short_lookback) - pd.ewma(
data, long_lookback)
# Rolling z secore using longer lookback
zscore = calc_zscore(signal, long_lookback)
position = (zscore / (vol * mul))
return position.shift(1)
def get_raw_value(self):
rtns = wind.get_wind_data("AShareEODPrices", "s_dq_pctchange").loc["2005-01-01":] / 100
beta = Descriptor.Beta().get_raw_value()
resid = {}
common_index = sorted(set(rtns.index) & set(beta.index))
R = get_estimation_universe().get_returns()
for idx in common_index:
row = rtns.loc[idx]
resid[idx] = row - beta.loc[idx] * R.loc[idx]
resid = pd.DataFrame(resid).T
sigma = pd.ewmstd(resid, halflife=self.T)
return sigma
def ewma_mom_daily_signal(data,
short_lookback,
long_lookback,
vol_lookback=20):
vol = pd.ewmstd(data, vol_lookback,
min_periods=vol_lookback) * math.sqrt(256)
signal = signal = pd.ewma(data, short_lookback) - pd.ewma(
data, long_lookback)
# Rolling z secore using longer lookback
zscore = calc_zscore(signal, long_lookback)
position = (zscore / (vol))
return position.shift(1)
def volatility(self, n, freq=None, which='close', ann=True, model='ln', min_periods=1, rolling='simple'):
"""Return the annualized volatility series. N is the number of lookback periods.
:param n: int, number of lookback periods
:param freq: resample frequency or None
:param which: price series to use
:param ann: If True then annualize
:param model: {'ln', 'pct', 'bbg'}
ln - use logarithmic price changes
pct - use pct price changes
bbg - use logarithmic price changes but Bloomberg uses actual business days
:param rolling:{'simple', 'exp'}, if exp, use ewmstd. if simple, use rolling_std
:return:
"""
if model not in ('bbg', 'ln', 'pct'):
raise ValueError('model must be one of (bbg, ln, pct), not %s' % model)
if rolling not in ('simple', 'exp'):
raise ValueError('rolling must be one of (simple, exp), not %s' % rolling)
px = self.frame[which]
px = px if not freq else px.resample(freq, how='last')
if model == 'bbg' and periods_in_year(px) == 252:
# Bloomberg uses business days, so need to convert and reindex
orig = px.index
px = px.resample('B').ffill()
chg = np.log(px / px.shift(1))
chg[chg.index - orig] = np.nan
if rolling == 'simple':
vol = pd.rolling_std(chg, n, min_periods=min_periods).reindex(orig)
else:
vol = pd.ewmstd(chg, span=n, min_periods=n)
return vol if not ann else vol * np.sqrt(260)
else:
chg = px.pct_change() if model == 'pct' else np.log(px / px.shift(1))
if rolling == 'simple':
vol = pd.rolling_std(chg, n, min_periods=min_periods)
else:
vol = pd.ewmstd(chg, span=n, min_periods=n)
return vol if not ann else vol * np.sqrt(periods_in_year(vol))
def robust_vol_calc(x, days=35, min_periods=10, vol_abs_min=0.0000000001, vol_floor=True,
floor_min_quant=0.05, floor_min_periods=100,
floor_days=500):
vol = pd.ewmstd(x, span=days, min_periods=min_periods)
vol[vol < vol_abs_min] = vol_abs_min
if vol_floor:
vol_min = pd.rolling_quantile(
vol, floor_days, floor_min_quant, floor_min_periods)
vol_min.set_value(vol_min.index[0], 0.0)
vol_min = vol_min.ffill()
vol_with_min = pd.concat([vol, vol_min], axis=1)
vol_floored = vol_with_min.max(axis=1, skipna=False)
else:
vol_floored = vol
return vol_floored
def trend(self, tags, top_n=None, other=False, resample='D', cumulative=False, ewmaspan=None):
""" show the supplied tags summed up per day """
if top_n is not None:
tags = self.top_n_tags(top_n, tags)
D = self.D[tags] if tags is not None else self.D
if other:
D['other'] = self.D[[t for t in self.D.keys()
if t not in tags]].sum(axis=1)
D = D.resample(resample, how='sum', label='left')
self._obfuscate(D)
D = D.fillna(0)
if ewmaspan is not None:
ewma = pd.ewma(D, span=ewmaspan)
ewmstd = pd.ewmstd(D, span=2 * ewmaspan)
if cumulative:
ewmstd = ewmstd * 3
ewma = ewma.cumsum()
if cumulative:
D = D.cumsum()
alpha = 0.5 if not cumulative and ewmaspan is not None else 1
ax = D.plot(linewidth=2, colormap=self.cmapname,
legend=False, alpha=alpha)
if ewmaspan is not None:
colors = self.cmap(np.linspace(0., 1., len(D.keys())))
if cumulative:
for idx, k in enumerate(tags):
ax.fill_between(D.index, np.array(ewma[k] + ewmstd[k]).ravel(),
np.array(ewma[k] - ewmstd[k]).ravel(),
facecolor=colors[idx], alpha=0.2,
linewidth=1)
ewma.plot(style='--', legend=False, ax=ax,
colormap=self.cmapname, linewidth=2)
ax.legend(ax.lines[:len(D.keys())],
map(lambda x:x.get_label(), ax.lines[:len(D.keys())]), loc='best')
ax.grid(True)
ax.set_ylim(0, D.max().max())
if cumulative:
plt.ylabel('Time Spent (h)')
else:
plt.ylabel('Time Spent (h) per Interval (%s)' % resample)
plt.xlabel('Interval ID')
def reversal_pl(btc_ret, long_only=True):
model = pd.ols(y=btc_ret,
x=btc_ret.shift(1),
window=12 * 24 * 5,
window_type='rolling')
betas = model.beta['x']
signal = timing_curve(betas * zscore(btc_ret))
sigma = np.sqrt(365 * 24 * 12) * pd.ewmstd(
btc_ret, com=12 * 24 * 5, min_periods=12 * 24 * 5)
if long_only:
view = (.3 * (signal) / sigma).clip(0, 9999999)
else:
view = (.3 * (signal) / sigma)
t_cost = (view.diff()).abs() * .005
pl = view.shift(1) * btc_ret
net_pl = view.shift(1) * (btc_ret) - t_cost
turnover = 365 * 12 * 24 * view.diff().abs().mean()
print calc_sharpe(pl)
print calc_sharpe(net_pl)
print turnover
return pd.DataFrame({'view': view, 'pl': pl, 'net_pl': net_pl})
def emstd(data, window):
"""Exponential Moving Standard Deviation: The exponentially weighted
standard deviation of the price of a security over a specific number of
periods.
'data' is a pandas Series or DataFrame of prices. A ValueError is raised
if 'data' is of different data type.
'window' is the number of observations.
It must be a positive integer less than or equal to the length of the data.
Otherwise a ValueError will be raised.
"""
# todo: maybe add 'long' too?
if not isinstance(window, int) or not 0 < window <= len(data):
raise ValueError("'window' must be an integer " +
"between 1 and %d." % len(data))
if not isinstance(data, (pd.Series, pd.DataFrame)):
raise ValueError("'data' must be a pandas Series or DataFrame.")
return pd.ewmstd(data, span = window)
def emstd(data, window):
"""Exponential Moving Standard Deviation: The exponentially weighted
standard deviation of the price of a security over a specific number of
periods.
'data' is a pandas Series or DataFrame of prices. A ValueError is raised
if 'data' is of different data type.
'window' is the number of observations.
It must be a positive integer less than or equal to the length of the data.
Otherwise a ValueError will be raised.
"""
# todo: maybe add 'long' too?
if not isinstance(window, int) or not 0 < window <= len(data):
raise ValueError("'window' must be an integer " +
"between 1 and %d." % len(data))
if not isinstance(data, (pd.Series, pd.DataFrame)):
raise ValueError("'data' must be a pandas Series or DataFrame.")
return pd.ewmstd(data, span=window)
plt.show()
#d1=pd.datetime(2007,1,1)
#d2=pd.datetime(2009,12,31)
nerpu=data.apply(find_datediff, axis=1)
nerpu.plot()
plt.title("Nerpu")
plt.show()
## Shouldn't need changing
vol_lookback=25
stdev_returns=pd.ewmstd(price - price.shift(1), span=vol_lookback)
ann_stdev=stdev_returns*ROOT_DAYS_IN_YEAR
raw_carry=nerpu/ann_stdev
f_scalar=30.0
raw_carry.plot()
plt.title("Raw carry")
plt.show()
forecast=raw_carry*f_scalar
c_forecast=cap_series(forecast)
data_to_plot=pd.concat([forecast,c_forecast], axis=1)
data_to_plot.columns=['Forecast','Capped forecast']
def calc_zscore(df,
mean_halflife=21,
mean_seed_period=21,
std_halflife=21,
std_seed_period=21,
smth_halflife=0,
ewm=True,
subtract_mean=True,
cap=3.0,
lag=0):
"""
Calculate timeseries z-score (assuming normal distribution of input data)
Parameters
----------
df : DataFrame or Series
DataFrame or Series object containing timeseries data
mean_halflife : int, optional
Half-life period (periodicity determined by index of df) for computing mean
mean_seed_period : int, optional
Seeding period (periodicity determined by index of df) for computing mean
std_halflife : int, optional
Half-life period (periodicity determined by index of df) for computing standard deviation
std_seed_period : int, optional
Seeding period (periodicity determined by index of df) for computing standard deviation
smth_halflife : int, optional
Smoothing half-life period (periodicity determined by index of df) for smoothing input data before computing z-score
ewm : bool, optional
If True, compute z-score based on ewm mean and standard deviation. If False, compute z-score based on simple mean and standard deviation.
subtract_mean : bool, optional
If True, subtract mean while computing z-score. If False, normalize the value by dividing by standard deviation.
cap : float, optional
Absolute cap for z-score
lag : int, optional
Periods (periodicity determined by index of df) by which to lag the z-score
Returns
-------
score_df : DataFrame or Series
DataFrame or Series object containing z-score
"""
is_series = False
if isinstance(df, pd.Series):
df = pd.DataFrame(df)
is_series = True
elif not isinstance(df, pd.DataFrame):
raise ValueError('df should be either a DataFrame or Series object')
if mean_halflife < 0:
raise ValueError('%d is not a valid mean half-life' % mean_halflife)
if mean_halflife > df.shape[0]:
raise ValueError('mean_halflife can not be larger than length of index of df')
if mean_seed_period < 0:
raise ValueError('%d is not a valid mean seed period' % mean_seed_period)
if mean_seed_period > df.shape[0]:
raise ValueError('mean_seed_period can not be larger than length of index of df')
if std_halflife < 0:
raise ValueError('%d is not a valid standard deviation half-life' % std_halflife)
if std_halflife > df.shape[0]:
raise ValueError('std_halflife can not be larger than length of index of df')
if std_seed_period < 0:
raise ValueError('%d is not a valid standard deviation seed period' % std_seed_period)
if std_seed_period > df.shape[0]:
raise ValueError('std_seed_period can not be larger than length of index of df')
if smth_halflife < 0:
raise ValueError('%d is not a valid smoothing half-life' % smth_halflife)
if smth_halflife > df.shape[0]:
raise ValueError('smth_halflife can not be larger than length of index of df')
if not isinstance(ewm, bool):
raise ValueError('ewm should be either True of False')
if not isinstance(subtract_mean, bool):
raise ValueError('subtract_mean should be either True of False')
if cap <= 0:
raise ValueError('%f is not a valid score cap' % cap)
if lag < 0:
raise ValueError('%d is not a valid lag period' % lag)
if lag > df.shape[0]:
raise ValueError('lag can not be larger than length of index of df')
# apply smoothing
if smth_halflife > 0:
df = pd.ewma(df, halflife=smth_halflife, min_periods=smth_halflife, adjust=False)
# compute mean and standard deviation
if ewm:
mean_df = pd.ewma(df, halflife=mean_halflife, min_periods=mean_seed_period, adjust=False)
std_df = pd.ewmstd(df, halflife=std_halflife, min_periods=std_seed_period, adjust=False)
else:
mean_df = pd.rolling_mean(df, window=mean_halflife, min_periods=mean_seed_period)
std_df = pd.rolling_std(df, window=std_halflife, min_periods=std_seed_period)
# compute score
if subtract_mean:
score_df = (df - mean_df) / std_df
else:
score_df = df / std_df
# cap score
score_df = score_df.clip(-cap, cap)
# lag score
if lag > 0:
score_df = score_df.shift(lag)
if is_series:
return pd.Series(score_df.squeeze())
else:
return score_df
def devol(self, _lambda=0.06, n_days=1):
_com = (1 - _lambda) / _lambda
self.df['LogReturns'] = np.log(self.df.Close.pct_change(periods=n_days) + 1)
self.df['Vola'] = pd.ewmstd( self.df.LogReturns, com=_com, ignore_na=True)[2:]
self.df['DevolLogReturns'] = self.df.LogReturns / self.df.Vola
self.df.set_index('Date', inplace=True)
def volatility(price, vol_lookback=25):
return pd.ewmstd(price - price.shift(1), span=vol_lookback, min_periods=vol_lookback)
def addEWSDev(self, fromIndex, addedSeriesName = 'ewstdev', win_length = 1):
self[addedSeriesName] = pd.ewmstd(self[fromIndex], win_length)
def smoothSeriesEwmvar(self, series, span=5.0, adjust=True, halflife=None, min_periods=0):
return pandas.ewmstd(
series, com=None, span=span, halflife=halflife, min_periods=min_periods, adjust=adjust, ignore_na=True
)
def spread_crossover(data_df,slow=1,fast=12):
spread_log = pd.DataFrame(np.log(data_df.ix[:,0] * 100))
data_df['spread_z_ma'] = (spread_log - pd.expanding_mean(spread_log, min_periods=24))/ pd.expanding_std(spread_log, min_periods=24)
data_df['spread_z_ema'] = (spread_log - pd.ewma(spread_log, min_periods = 24, halflife=12)) / pd.ewmstd(spread_log, halflife=12)
data_df['spread_z_ema'] = pd.rolling_mean(data_df['spread_z_ema'], window=3)
data_df['slow'] = pd.rolling_mean(data_df['US HY Spread'],slow)
data_df['fast'] = pd.rolling_mean(data_df['US HY Spread'],fast)
data_df['diff'] = (data_df['slow'] - data_df['fast']) * -1
data_df['diff'] = data_df['diff'] + 1
data_df['diff'] = np.log(data_df['diff'])
data_df['tren_z_ma'] = (data_df['diff'] - pd.expanding_mean(data_df['diff'], min_periods=24))/ pd.expanding_std(data_df['diff'], min_periods=24)
data_df['tren_z_ma'] = pd.rolling_mean(data_df['tren_z_ma'], window=3)
trend_valuation_df = pd.concat([data_df['spread_z_ema'],data_df['tren_z_ma']], axis=1)
trend_valuation_df.dropna(inplace=True)
trend_valuation_df.plot()
plt.show()
algo_wghts_df = pd.DataFrame()
wghts_array = []
valuation_threshold_cheap = 1
valuation_threshold_rich = -1.0
trend_threshold_tightening = 0.1
trend_threshold_widening = -0.1
data_df['spread_z_ma'].plot()
plt.show()
for score in trend_valuation_df.values:
valuation_score = score[0]
trend_score = score[1]
if (trend_score >= -0.2 and valuation_score >= -1):
wghts_array.append(min(1,abs(trend_score-valuation_score) / 1))
else:
wghts_array.append(0)
#elif trend_score <= -0.1 and valuation_score <= valuation_threshold_cheap:
# wghts_array.append(-1)
#elif valuation_score >= valuation_threshold_cheap:
# wghts_array.append(1)
#else:
# wghts_array.append(0)
wghts_df = pd.DataFrame(wghts_array, index = trend_valuation_df.index)
long = wghts_df[wghts_df == 1].count()[0] / len(trend_valuation_df)
neutral = wghts_df[wghts_df == 0].count()[0] / len(trend_valuation_df)
short = wghts_df[wghts_df == -1].count()[0] / len(trend_valuation_df)
wghts_df.columns = [data_df.columns.values[1]]
wghts_df = wghts_df.shift(1)
s1 = bt.Strategy('Valuation & Trend ', [bt.algos.WeighTarget(wghts_df),
bt.algos.Rebalance()])
return_data = data_df.ix[:,1].to_frame()
return_data.columns = [data_df.columns.values[1]]
strategy = bt.Backtest(s1, return_data)
res = bt.run(strategy)
res.plot(logy=True)
res.display()
print(long,neutral,short)
"""Now let's get some real data using quandl"""
startDate = "2017-01-01"
endDate = "2017-12-31"
df = qd.get("WIKI/F", start_date=startDate, end_date=endDate)
time = np.linspace(1, len(df['Adj. Close']), len(df['Adj. Close']))
returns = pd.Series.diff(df['Adj. Close']) / df['Adj. Close']
# Shift so that we're predicting today's close price from yesterday's returns:
returns = returns.shift(-1)[:-1]
k = 5 # Rolling average length
sigma = pd.ewmstd(returns, span=k) * k # 'diffusion' coefficient
mu = pd.ewma(returns, span=k) * k # 'drift' coefficient
#del sigma.index.name
# New length:
N = len(df['Adj. Close'])
# Number of sims to run:
M = 100
XN = np.zeros([M, N])
# New time steps:
dt = 1. / N
""" NOT USED """
#xn = np.zeros(N)
#xn[k] = df['Adj. Close'][k]
#
def parse(date, mode):
conn = sqlite3.connect('data/orderbook_' + date + '.db')
freq = '5S'
cursor = conn.cursor()
# resLst=cursor.execute(SELECT_SQL, (exchange,pair))
okex_df = pd.read_sql_query(SELECT_SQL,
conn,
params=('okex', ),
index_col='timestamp')
# df = pd.read_sql_table('trades',conn,)
okex_df.index = pd.to_datetime(okex_df.index / 1000, unit='s')
# print(df['price'].resample('1H').ohlc().tail())
# print(df['amount'].resample('1H').sum().tail())
# print(df['price'].resample('1H').mean().tail())
# okex_mean_serial= okex_df['price'].resample(freq).mean()
# okex_mean_serial.name='okex'
poloniex_df = pd.read_sql_query(SELECT_SQL,
conn,
params=('poloniex', ),
index_col='timestamp')
poloniex_df.index = pd.to_datetime(poloniex_df.index / 1000, unit='s')
conn.close()
if mode == 1:
res_df = pd.concat([okex_df, poloniex_df], axis=1)
res_df.apply(exchange, axis=1)
print(x)
elif mode == 2:
profit_ok_sell = 2 * (
okex_df['bid1'] - poloniex_df['ask1'] -
(okex_df['bid1'] * 0.002 + poloniex_df['ask1'] * 0.0025)) / (
okex_df['bid1'] + poloniex_df['ask1'])
# first=okex_df['bid1']-poloniex_df['ask1']
profit_ok_sell.name = 'ok sell and poloniex buy'
profit_ok_buy = 2 * (
poloniex_df['bid1'] - okex_df['ask1'] -
(okex_df['ask1'] * 0.002 + poloniex_df['bid1'] * 0.0025)) / (
poloniex_df['bid1'] + okex_df['ask1'])
# second=poloniex_df['bid1']-okex_df['ask1']
profit_ok_buy.name = 'poloniex sell and ok buy'
res_df = pd.concat([profit_ok_sell, profit_ok_buy], axis=1)
# res_df.plot()
# plt.figure();
res_df.plot.hist(bins=20, alpha=0.5)
# cost_ok_sell=okex_df['bid1']*0.001+poloniex_df['ask1']*0.0015
# cost_ok_sell.name='cost_ok_sell'
# cost_ok_buy=okex_df['ask1']*0.001+poloniex_df['bid1']*0.0015
# cost_ok_buy='cost_ok_buy'
# # res_df=pd.concat([cosk_ok_sell,cost_ok_buy],axis=1)
# res_df=cost_ok_sell/profit_ok_sell
# res_df.plot()
plt.show()
elif mode == 3:
profit_ok_sell = okex_df['bid1'] - poloniex_df['ask1'] - (
okex_df['bid1'] * 0.002 + poloniex_df['ask1'] * 0.0025)
profit_ok_sell.name = 'ok sell and poloniex buy'
profit_ok_buy = poloniex_df['bid1'] - okex_df['ask1'] - (
okex_df['ask1'] * 0.002 + poloniex_df['bid1'] * 0.0025)
# second=poloniex_df['bid1']-okex_df['ask1']
profit_ok_buy.name = 'poloniex sell and ok buy'
res_df = pd.concat([profit_ok_sell, profit_ok_buy], axis=1)
res_df.plot()
plt.show()
# plt.figure();
# res_df.plot.hist(bins=20,alpha=0.5)
elif mode == 4: #EWMA
profit_ok_sell = okex_df['bid1'] - poloniex_df['ask1']
profit_ok_sell.name = 'ok sell and poloniex buy'
profit_ok_sell.name = 'ok sell and poloniex buy'
profit_ok_buy = poloniex_df['bid1'] - okex_df['ask1']
# second=poloniex_df['bid1']-okex_df['ask1']
profit_ok_buy.name = 'poloniex sell and ok buy'
span = 20
freq = '1H'
ewma = pd.ewma(profit_ok_sell, span=span, freq=freq, adjust=True)
ewma.name = 'oloniex buy ewma1'
ewmstd = pd.ewmstd(profit_ok_sell, span=span, freq=freq)
ewmstd.name = 'oloniex buy ewmstd1'
ewma2 = pd.ewma(profit_ok_buy, span=span, freq=freq, adjust=True)
ewma2.name = 'ok buy ewma2'
ewmstd2 = pd.ewmstd(profit_ok_buy, span=span, freq=freq)
ewmstd2.name = 'ok buy ewmstd2'
upper = ewma + 1.5 * ewmstd
upper.name = 'poloniex buy'
lower = ewma2 + 1.5 * ewmstd2
lower.name = 'ok buy thres'
res_df = pd.concat([ewma, upper, ewma2, lower], axis=1)
res_df.plot()
plt.show()
def calc_zscore_ew(df, lookback=24):
return (df - pd.ewma(df, lookback, min_periods=12)) / pd.ewmstd(
df, lookback, min_periods=12)
def calc_zscore(df,
mean_halflife=21,
mean_seed_period=21,
std_halflife=21,
std_seed_period=21,
smth_halflife=0,
ewm=True,
subtract_mean=True,
cap=3.0,
lag=0):
"""
Calculate timeseries z-score (assuming normal distribution of input data)
Parameters
----------
df : DataFrame or Series
DataFrame or Series object containing timeseries data
mean_halflife : int, optional
Half-life period (periodicity determined by index of df) for computing mean
mean_seed_period : int, optional
Seeding period (periodicity determined by index of df) for computing mean
std_halflife : int, optional
Half-life period (periodicity determined by index of df) for computing standard deviation
std_seed_period : int, optional
Seeding period (periodicity determined by index of df) for computing standard deviation
smth_halflife : int, optional
Smoothing half-life period (periodicity determined by index of df) for smoothing input data before computing z-score
ewm : bool, optional
If True, compute z-score based on ewm mean and standard deviation. If False, compute z-score based on simple mean and standard deviation.
subtract_mean : bool, optional
If True, subtract mean while computing z-score. If False, normalize the value by dividing by standard deviation.
cap : float, optional
Absolute cap for z-score
lag : int, optional
Periods (periodicity determined by index of df) by which to lag the z-score
Returns
-------
score_df : DataFrame or Series
DataFrame or Series object containing z-score
"""
is_series = False
if isinstance(df, pd.Series):
df = pd.DataFrame(df)
is_series = True
elif not isinstance(df, pd.DataFrame):
raise ValueError('df should be either a DataFrame or Series object')
if mean_halflife < 0:
raise ValueError('%d is not a valid mean half-life' % mean_halflife)
if mean_halflife > df.shape[0]:
raise ValueError(
'mean_halflife can not be larger than length of index of df')
if mean_seed_period < 0:
raise ValueError('%d is not a valid mean seed period' %
mean_seed_period)
if mean_seed_period > df.shape[0]:
raise ValueError(
'mean_seed_period can not be larger than length of index of df')
if std_halflife < 0:
raise ValueError('%d is not a valid standard deviation half-life' %
std_halflife)
if std_halflife > df.shape[0]:
raise ValueError(
'std_halflife can not be larger than length of index of df')
if std_seed_period < 0:
raise ValueError('%d is not a valid standard deviation seed period' %
std_seed_period)
if std_seed_period > df.shape[0]:
raise ValueError(
'std_seed_period can not be larger than length of index of df')
if smth_halflife < 0:
raise ValueError('%d is not a valid smoothing half-life' %
smth_halflife)
if smth_halflife > df.shape[0]:
raise ValueError(
'smth_halflife can not be larger than length of index of df')
if not isinstance(ewm, bool):
raise ValueError('ewm should be either True of False')
if not isinstance(subtract_mean, bool):
raise ValueError('subtract_mean should be either True of False')
if cap <= 0:
raise ValueError('%f is not a valid score cap' % cap)
if lag < 0:
raise ValueError('%d is not a valid lag period' % lag)
if lag > df.shape[0]:
raise ValueError('lag can not be larger than length of index of df')
# apply smoothing
if smth_halflife > 0:
df = pd.ewma(df,
halflife=smth_halflife,
min_periods=smth_halflife,
adjust=False)
# compute mean and standard deviation
if ewm:
mean_df = pd.ewma(df,
halflife=mean_halflife,
min_periods=mean_seed_period,
adjust=False)
std_df = pd.ewmstd(df,
halflife=std_halflife,
min_periods=std_seed_period,
adjust=False)
else:
mean_df = pd.rolling_mean(df,
window=mean_halflife,
min_periods=mean_seed_period)
std_df = pd.rolling_std(df,
window=std_halflife,
min_periods=std_seed_period)
# compute score
if subtract_mean:
score_df = (df - mean_df) / std_df
else:
score_df = df / std_df
# cap score
score_df = score_df.clip(-cap, cap)
# lag score
if lag > 0:
score_df = score_df.shift(lag)
if is_series:
return pd.Series(score_df.squeeze())
else:
return score_df
# print playerData
total_points = []
total_salary = []
unadjusted_points = []
for row in playerData.iterrows():
oppt = row[1]['Oppt']
week = row[1]['Week']
year = row[1]['Year']
pos = row[1]['Pos']
if oppt == '-':
continue
#FFPG[int(row[0])] = np.mean(total_points[-win_size:])
if EWMA and len(total_points) > 0:
FFPG[int(row[0])] = pd.ewma(pd.Series(total_points), span = win_size).values[-1]
price[int(row[0])] = pd.ewma(pd.Series(total_salary), span = win_size).values[-1]
FDStd[int(row[0])] = pd.ewmstd(pd.Series(unadjusted_points), span = win_size).values[-1]
# ARIMA models need at least 6 points to adequately fit the data
elif ARIMA and len(total_points) >= 6 and np.mean(total_points) > 10:
# the input mathematica string needs to be
# formatted like {a,b,c}
cmdString = '{'
for elem in total_points:
cmdString += str(elem) + ','
cmdString = cmdString[:-1]
cmdString += '}'
# calls the mathematica script
command = '/usr/local/bin/MathematicaScript -script ~/FSA/1iaFantasy/mathTimeseries.sh %s' %cmdString
# reads the output of the mathematica script
# that is printed to the terminal
output = os.popen(command).read().split('\n')
print output
country="US"
yearband=5 ## blocks to divide data into
#datatype="Three_assets"
datatype="Developed_equities"
if datatype=="Three_assets":
data, initial_stdev, initial_risk_weights, yields = get_some_crossasset_data()
elif datatype=="Developed_equities":
data, initial_stdev, initial_risk_weights, yields = get_equities_data(case="devall")
else:
raise Exception()
## rolling stdev for estimates if used
stdevest=pd.ewmstd(data, halflife=12)*(12**.5)
for idx in range(12):
stdevest.iloc[idx,:]=initial_stdev
stdevest[stdevest==0]=np.nan
#bootstrapped_weights=optimise_over_periods(data, "rolling", "bootstrap", rollyears=5, equalisemeans=True, equalisevols=True,
# monte_carlo=50, monte_length=12*5)
bootstrapped_weights=optimise_over_periods(data, "rolling", "shrinkage", rollyears=5, equalisevols=True,
shrinkage_factors=(1.0, 0.8))
avg_size=1.0/len(data.columns)
#windowsizes=[0.5]
ABuMarketDrawing.plot_candle_form_klpd(tsla_df, html_bk=True)
# 使用 Pandas 可视化数据
demo_list = np.array([2, 4, 16, 20])
demo_window = 3
pd.rolling_std(demo_list, window=demo_window,
center=False) * np.sqrt(demo_window)
tsla_df_copy = tsla_df.copy()
# 计算投资回报
tsla_df_copy['return'] = np.log(tsla_df['close'] / tsla_df['close'].shift(1))
# 移动收益标准差
tsla_df_copy['mov_std'] = pd.rolling_std(
tsla_df_copy['return'], window=20, center=False) * np.sqrt(20)
# 加权移动收益标准差
tsla_df_copy['std_ewm'] = pd.ewmstd(
tsla_df_copy['return'], span=20, min_periods=20, adjust=True) * np.sqrt(20)
tsla_df_copy[['close', 'mov_std', 'std_ewm', 'return']].plot(subplots=True,
grid=True)
# 绘制均线
tsla_df.close.plot()
# ma 30
pd.rolling_mean(tsla_df.close, window=30).plot()
# ma 60
pd.rolling_mean(tsla_df.close, window=60).plot()
# ma 90
pd.rolling_mean(tsla_df.close, window=90).plot()
plt.legend(['close', '30 mv', '60 mv', '90 mv'], loc='best')
# 验证低开高走第二天趋势
NBER_rec.index.name='date'
for ticker in ticker_list:
if ticker != 'USURTOT Index':
continue
NBER_rec_temp = NBER_rec.copy()
temp_tick_data = bl_data[ticker].to_frame()
temp_tick_data.dropna(inplace=True)
look_back_sma = 24
hl_ewm = 6
ticker_ma = (temp_tick_data-pd.rolling_mean(temp_tick_data,window=look_back_sma))/pd.rolling_std(temp_tick_data,window=look_back_sma)
ticker_z_ema = (temp_tick_data-pd.ewma(temp_tick_data,halflife=hl_ewm))/pd.ewmstd(temp_tick_data,halflife=hl_ewm)
recession_level, expansion_level = scenario(ticker,NBER_rec_temp, temp_tick_data)
recession_ma, expansion_ma = scenario(ticker,NBER_rec_temp, ticker_ma)
recession_ema, expansion_ema = scenario(ticker,NBER_rec_temp, ticker_z_ema)
#plot
new_idx = pd.date_range(temp_tick_data.index.to_datetime()[0],pd.datetime.today().date(),freq='B')
temp_tick_data = temp_tick_data.reindex(new_idx,method='ffill')
NBER_rec_temp = NBER_rec_temp.reindex(new_idx,method='ffill')
def spread_val_score(data_df):
data_df = np.log(data_df * 100)
data_df['spread_z_ema'] = (data_df - pd.ewma(data_df, min_periods = 60, halflife=60)) / pd.ewmstd(data_df, halflife=60)
data_df['spread_z_ema'].dropna(inplace=True)
data_df['spread_z_ema'].plot()
plt.show()
return data_df['spread_z_ema']
data_index = pd.DataFrame()
for m in mkts.keys():
try:
data_index[m] = quandl.get(mkts[m], authtoken=token).Last
except:
try:
data_index[m] = quandl.get(mkts[m], authtoken=token).Settle
except:
try:
data_index[m] = quandl.get(mkts[m], authtoken=token).Value
except:
try:
data_index[m] = quandl.get(mkts[m], authtoken=token).value
except:
try:
data_index[m] = quandl.get(mkts[m],
authtoken=token).Rate
except:
print(m)
data_pct = data_index.pct_change()
mu = pd.ewma(data_pct, 260)
sd = pd.ewmstd(data_pct, 260)
zscores = (data_pct - mu) / sd
last = zscores.iloc[-2].dropna().sort_values()
last.plot(kind='barh', colormap='jet',
ylim=[-3, 3]).get_figure().savefig('zscore.png', bbox_inches='tight')
e = Email(subject='Morning Update: Macro Dashboard')
e.add_attachment('zscore.png')
e.send()
def volatility(price, vol_lookback):
price['volatility'] = 0
price['volatility'] = pd.ewmstd((price['Close'] - price['Close'].shift(1)), span=vol_lookback)
price['volatility'] = price['volatility'].fillna(0)
return price
def devol(self, _lambda=0.06):
_com = (1 - _lambda) / _lambda
self.ts['Vola'] = pd.ewmstd( self.ts.LogReturns, com=_com, ignore_na=True)
self.ts['DevolLogReturns'] = self.ts.LogReturns / self.ts.Vola
def addBollBand_EW(self, fromIndex, addedSeriesName = 'BBand', scale=1,win_length = 1):
self[addedSeriesName+'upper'] = scale*pd.ewmstd(self[fromIndex], win_length) + self[fromIndex]
self[addedSeriesName+'lower'] = -scale*pd.ewmstd(self[fromIndex], win_length) + self[fromIndex]
def ewbband(self, halflife): '''Create Expontenial Weighted Bollinger Band.''' self.df[self.symbol+'.ewma'] = pd.ewma(self.df[self.close_index].shift(1), halflife) self.df[self.symbol+'.ewmstd'] = pd.ewmstd(self.df[self.close_index].shift(1), halflife) self.df[self.symbol+'.ewbb_upper'] = self.df[self.symbol+'.ewma'] + self.df[self.symbol+'.ewmstd'] self.df[self.symbol+'.ewbb_lower'] = self.df[self.symbol+'.ewma'] - self.df[self.symbol+'.ewmstd']
fast_ewma = pd.ewma(price, span=Lfast)
slow_ewma = pd.ewma(price, span=Lslow)
raw_ewmac = fast_ewma - slow_ewma
data_to_plot = pd.concat([price, fast_ewma, slow_ewma], axis=1)
data_to_plot.columns = ['Price', 'Fast', 'Slow']
data_to_plot[d1:d2].plot()
plt.show()
raw_ewmac[d1:d2].plot()
plt.title("Raw EWMAC")
plt.show()
## volatility adjustment
stdev_returns = pd.ewmstd(price - price.shift(1), span=vol_lookback)
vol_adj_ewmac = raw_ewmac / stdev_returns
vol_adj_ewmac[d1:d2].plot()
plt.title("Vol adjusted")
plt.show()
## scaling adjustment
f_scalar = ewmac_forecast_scalar(Lfast, Lslow)
forecast = vol_adj_ewmac * f_scalar
cap_forecast = cap_series(forecast, capmin=-20.0, capmax=20.0)
data_to_plot = pd.concat([forecast, cap_forecast], axis=1)
data_to_plot.columns = ['Scaled Forecast', 'Capped forecast']
def std_space(s, years):
return pd.ewmstd(s, halflife=360*years)