def __init__(self, lags=10, mode="abs"):
self.lags = lags
modes = ["abs", "square"]
if mode not in modes:
raise ValueError("`mode` must be one of " + str(modes) + ".")
self.mode = mode
self.mr = MinutelyReturns()
super().__init__()
class Autocorrelation(Metric):
def __init__(self, lag=1, window=30):
self.lag = lag
self.window = window
self.mr = MinutelyReturns()
super().__init__()
def compute(self, df):
df = pd.Series(self.mr.compute(df))
df = df.rolling(self.window,
center=True).apply(lambda x: x.autocorr(lag=self.lag),
raw=False)
return df.dropna().tolist()
def visualize(self, simulated, real, plot_real=True):
min_sim = min([len(x) for x in simulated.values()])
min_size = min(min_sim, len(real))
random.shuffle(real)
real = real[:min_size]
for k, v in simulated.items():
random.shuffle(v)
simulated[k] = v[:min_size]
self.hist(simulated,
real,
title="Autocorrelation (lag={}, window={})".format(
self.lag, self.window),
xlabel="Correlation coefficient",
log=False,
plot_real=plot_real)
class VolatilityClustering(Metric):
def __init__(self, lags=10, mode="abs"):
self.lags = lags
modes = ["abs", "square"]
if mode not in modes:
raise ValueError("`mode` must be one of " + str(modes) + ".")
self.mode = mode
self.mr = MinutelyReturns()
super().__init__()
def compute(self, df):
df = pd.Series(self.mr.compute(df))
if self.mode == "abs":
df = abs(df)
elif self.mode == "square":
df = df**2
return [[df.autocorr(lag) for lag in range(1, self.lags + 1)]]
def visualize(self, simulated, real, plot_real=True):
self.line(simulated,
real,
"Volatility Clustering/Long Range Dependence",
"Lag",
"Correlation coefficient",
plot_real=plot_real)
class ReturnsVolatilityCorrelation(Metric):
def __init__(self, intervals=4):
self.mr = MinutelyReturns()
def compute(self, df):
returns = np.array(self.mr.compute(df))
volatility = abs(returns)
return [np.corrcoef(returns, volatility)[0,1]]
def visualize(self, simulated):
self.hist(simulated, title="Returns/Volatility Correlation", xlabel="Correlation coefficient", bins=50)
class VolumeVolatilityCorrelation(Metric):
def __init__(self, intervals=4):
self.mr = MinutelyReturns()
def compute(self, df):
volatility = abs(np.array(self.mr.compute(df)))
volume = df["volume"].iloc[1:].values
return [np.corrcoef(volume, volatility)[0, 1]]
def visualize(self, simulated):
self.hist(simulated,
title="Volume/Volatility Correlation",
xlabel="Correlation coefficient")
class AggregationNormality(Metric):
def __init__(self):
self.mr = MinutelyReturns()
def compute(self, df):
df = df[["close"]].resample("10T").last()
return self.mr.compute(df)
def visualize(self, simulated):
self.hist(simulated,
"Aggregation Normality (10 minutes)",
"Log Returns",
log=True,
clip=.05)
class Kurtosis(Metric):
def __init__(self, intervals=4):
self.intervals = intervals
self.mr = MinutelyReturns()
def compute(self, df):
ks = []
for i in range(1,self.intervals+1):
temp = df[["close"]].resample("{}T".format(i)).last()
rets = self.mr.compute(temp)
ks.append(kurtosis(rets))
return [ks]
def visualize(self, simulated):
self.line(simulated, title="Kurtosis", xlabel="Time scale (min)", ylabel="Average kurtosis", logy=True)
def __init__(self, intervals=4):
self.mr = MinutelyReturns()
def __init__(self, lag=1, window=30):
self.lag = lag
self.window = window
self.mr = MinutelyReturns()
super().__init__()
def __init__(self):
self.mr = MinutelyReturns()