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BTC.py
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BTC.py
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# -*- coding: utf-8 -*-
"""
Created on Mon Oct 10 14:08:34 2016
@author: d_floriello
BTC analysis
"""
import pandas as pd
from pandas.tools import plotting
import statsmodels.api
import numpy as np
import matplotlib.pyplot as plt
from collections import OrderedDict
import scipy.stats
btc = pd.read_csv('C:/Users/d_floriello/Documents/bitcoin (2).csv')
btc = btc.set_index(pd.DatetimeIndex(btc['Date']))
btc.head()
btc = btc[['Open', 'High', 'Low', 'Close']]
plt.figure()
btc['Close'].plot()
plt.figure()
plt.plot(statsmodels.api.tsa.periodogram(np.array(btc['Close'])))
plt.title('BTC periodogram')
btc_3 = btc.ix[btc.index.year >= 2013]
btc_3.plot()
plt.figure()
plt.plot(statsmodels.api.tsa.periodogram( (np.array(btc_3['Close']) - np.mean(np.array(btc_3['Close'])))/np.std(np.array(btc_3['Close'])) ))
plt.title('BTC periodogram (from 2013)')
btc_3M = btc_3.resample('M').mean()
plt.figure()
btc_3M.plot()
plt.figure()
plt.plot(statsmodels.api.tsa.periodogram( (np.array(btc_3M['Close']) - np.mean(np.array(btc_3M['Close'])))/np.std(np.array(btc_3M['Close'])) ))
plt.title('MBTC periodogram (from 2013)')
###############################################################################
def fourierExtrapolation(x, n_predict, n_harmonics = 0):
x = np.array(x)
n = x.size
if n_harmonics == 0:
n_harm = 100 # number of harmonics in model
else:
n_harm = n_harmonics
t = np.arange(0, n)
p = np.polyfit(t, x, 1) # find linear trend in x
x_notrend = x - p[0] * t # detrended x
x_freqdom = np.fft.fft(x_notrend) # detrended x in frequency domain
f = np.fft.fftfreq(n) # frequencies
indexes = list(range(n))
# sort indexes by frequency, lower -> higher
indexes.sort(key = lambda i: np.absolute(f[i]))
t = np.arange(0, n + n_predict)
restored_sig = np.zeros(t.size)
for i in indexes[:1 + n_harm * 2]:
ampli = np.absolute(x_freqdom[i]) / n # amplitude
phase = np.angle(x_freqdom[i]) # phase
restored_sig += ampli * np.cos(2 * np.pi * f[i] * t + phase)
return restored_sig + p[0] * t
###############################################################################
plt.figure()
plt.plot(np.array(btc_3M))
plt.plot(fourierExtrapolation(np.array(btc_3M).ravel(), 0, 2), color = 'red')
dl = np.diff(np.log(btc['Close']))
plt.figure()
plt.plot(dl)
d = np.diff(btc['Close'])
plt.figure()
plt.plot(d)
rng = pd.date_range(btc.index[0], btc.index[-1], freq = 'D')
ts = pd.DataFrame(btc['Close']).set_index(rng)
missing = []
for dti in btc.index:
if dti in rng:
print('{} is in rng: {}'.format(dti, dti in rng))
missing.append(dti)
set(rng).difference(set(missing))
## missing dates from 24-06-2016 to 23-07-2016
present = []
for ud in np.unique(btc.index):
if btc.ix[btc.index == ud].shape[0] > 1:
present.append(ud)
dec = statsmodels.api.tsa.seasonal_decompose(pd.Series(btc['Close']), freq = 28)
plt.figure()
dec.plot()
plt.figure()
plt.plot(np.array(dec.seasonal[0:28]))
change = pd.read_excel('C:/Users/d_floriello/Documents/change.xlsx')
change = change.set_index(change['Date'])
change = change['change']
plt.figure()
change.plot()
cd = set(change.index).intersection(btc.index)
ibtc = set(btc.index).intersection(cd)
tsbtc = pd.Series(btc['Close'].ix[list(ibtc)])
tsc = pd.Series(change.ix[list(cd)])
plt.figure()
tsbtc.plot()
plt.axhline(y = 200)
plt.axhline(y = 500)
plt.figure()
tsc.plot()
tsbtc.corr(tsc)
plt.figure()
plotting.lag_plot(tsbtc) ### surprising!!! I think the reticular squared structure puts in evidence the
### particular pattern tat I've noticed.
### N.B.: dates from 2013
data_bit = []
for i in range(tsbtc.size-1):
xy = np.array([tsbtc.ix[i],tsbtc.ix[i+1]])
data_bit.append(xy)
dataset = np.array(data_bit)
from sklearn import mixture
model = mixture.GMM(n_components = 9, covariance_type = 'full').fit(dataset)
x = np.linspace(0, 1200, num = 1200)
X, Y = np.meshgrid(x, x)
XX = np.array([X.ravel(), Y.ravel()]).T
Z = -model.score_samples(XX)[0]
Z = Z.reshape(X.shape)
#### Tuning GMM model:
covs = [ 'spherical', 'tied', 'diag', 'full']
nc = [4,9,12]
for c in covs:
for n in nc:
mGMM = mixture.GMM(n_components = n, covariance_type = c).fit(dataset)
print("""GMM model with {} covariance,
and {} components:
aic = {}
bic = {}
likelihood = {}""".format(c, n, mGMM.aic(dataset), mGMM.bic(dataset),
mGMM.score_samples(dataset)[0].sum()))
############## best: 12 components and full covariance
plt.figure()
CS = plt.contour(X, Y, Z, levels=np.logspace(0, 100, 10))
CB = plt.colorbar(CS, shrink=0.8, extend='both')
plt.scatter(dataset[:, 0], dataset[:, 1], .8)
plt.title('Negative log-likelihood predicted by a GMM')
plt.axis('tight')
plt.show()
###############################################################################
def conditional_distribution(df, xs, xe):
edf1 = df.ix[xs <= df[df.columns[0]].values]
edf = edf1.ix[edf1[edf1.columns[0]].values < xe]
#plt.figure()
#edf.hist()
return edf
###############################################################################
def simple_MarkovMatrix(df, xs, xe):
# http://stats.stackexchange.com/questions/14360/estimating-markov-chain-probabilities
# http://stats.stackexchange.com/questions/41145/simple-way-to-algorithmically-identify-a-spike-in-recorded-errors
diz = OrderedDict()
edf = conditional_distribution(df, xs, xe)
m0 = edf[0].min()
M0 = edf[0].max()
#dr0 = M0 - m0
#step0 = dr0/10
m1 = df[1].min()
M1 = df[1].max()
#dr1 = M1 - m1
#step1 = dr1/10
lin0 = np.linspace(m0, M0, 10)
lin1 = np.linspace(m1, M1, 10)
for i in range(lin0.size - 1):
cedf = conditional_distribution(edf, lin0[i], lin0[i+1])
sizer = cedf.shape[0]
vec = []
for j in range(lin1.size - 1):
cedf1 = cedf.ix[lin1[j] <= cedf[cedf.columns[1]].values]
cedf2 = cedf1.ix[cedf1[cedf1.columns[1]].values < lin1[j + 1]]
vec.append(cedf2.shape[0]/sizer)
diz[str(lin0[i])] = vec
df = pd.DataFrame.from_dict(diz, orient = 'index')
df.columns = [[str(lin1[j]) for j in range(lin1.size-1)]]
return df
###############################################################################
tsbtc = pd.Series(btc['Close'])
data_bit = []
for i in range(tsbtc.size-1):
xy = np.array([tsbtc.ix[i],tsbtc.ix[i+1]])
data_bit.append(xy)
dataset = np.array(data_bit)
df = pd.DataFrame(dataset)
t1 = conditional_distribution(df, 200, 500)
df1 = simple_MarkovMatrix(df, 200, 500)
df2 = simple_MarkovMatrix(df, df[0].min(), df[0].max())
###############################################################################
close = btc['Close']
dclose = np.diff(close)
plt.figure()
plt.plot(dclose)
ret = []
for i in range(close.size - 1):
ret.append((close.ix[i+1] - close.ix[i])/close.ix[i])
returns = np.array(ret)
plt.figure()
plt.plot(returns)
plt.axhline(y = scipy.stats.mstats.mquantiles(np.array(returns), prob = 0.975))
plt.axhline(y = scipy.stats.mstats.mquantiles(np.array(returns), prob = 0.025))
plt.figure()
plt.plot(statsmodels.api.tsa.acf(returns))
returns = pd.Series(returns)
plt.figure()
plotting.autocorrelation_plot(returns)
plt.figure()
plotting.lag_plot(returns)
plt.figure()
returns.hist()
plt.figure()
plt.plot(statsmodels.api.tsa.periodogram(np.array(returns)))
per = statsmodels.api.tsa.periodogram(np.array(returns))
per[per > 0.01].size
plt.figure()
plt.plot(np.array(returns), color = 'red')
plt.plot(fourierExtrapolation(np.array(returns), 0, 154))
plt.figure()
plt.plot(np.array(close))
### zoom
plt.figure()
plt.plot(np.array(close)[1240:1300], color = 'magenta')
plt.figure()
plt.plot(np.array(returns)[1600:1700], color = 'magenta', marker = 'o')
###############################################################################
def get_ROI(ts):
rets = []
for i in range(ts.size - 1):
rets.append((ts.ix[i+1] - ts.ix[i])/ts.ix[i])
return np.array(rets)
###############################################################################
def detect_trend_at_t(ts, start, end):
rets = get_ROI(ts)
rets= rets[start:end]
trend = []
for t in range(1, rets.size, 1):
rets2 = rets[:t]
trend.append((rets2[rets2 >= 0].size)/rets2.size)
return trend
###############################################################################
trend = detect_trend_at_t(close, 1600, 1700)
###############################################################################
def trend_is_changing(ts, window):
rets = get_ROI(ts)
diffs = np.diff(ts)
ct = []
for i in range(2*window, diffs.size - window, 1):
jump = diffs[i]
q = scipy.stats.mstats.mquantiles(diffs[(i-window):i], prob = [0.025, 0.975])
if jump < q[0] or jump > q[1]:
bef_rets = rets[(i-window):i]
aft_rets = rets[i:(i+window)]
num_pos = bef_rets[bef_rets > 0].size/bef_rets.size
num_pos_ = aft_rets[aft_rets > 0].size/aft_rets.size
if num_pos > 0.5 and num_pos_ < 0.5:
ct.append((i, 'negative change'))
elif num_pos < 0.5 and num_pos_ > 0.5:
ct.append((i, 'positive change'))
else:
pass
return ct
###############################################################################
E = trend_is_changing(close, 50)
points = []
for i in E:
points.append(i[0])
plt.figure()
plt.plot(np.array(close))
plt.scatter(np.array(points), np.array(close.ix[points]), color = 'black', marker = 'o')
qnts = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95, 0.99]
for q in qnts:
print(scipy.stats.mstats.mquantiles(dclose, prob = q))
ixs = []
for i in range(close.size - 1):
if close.ix[i+1] - close.ix[i] > 16.74506:
ixs.append(i)
plt.figure()
plt.plot(np.array(close))
plt.scatter(np.array(ixs), np.array(close.ix[ixs]), color = 'black', marker = 'o')
plt.figure()
plt.plot(np.linspace(0,2280,2280), np.repeat(0, 2280))
plt.scatter(np.array(ixs), np.repeat(0, len(ixs)), color = 'black', marker = 'o')
#### on returns:
qnts = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95, 0.99]
for q in qnts:
print(scipy.stats.mstats.mquantiles(returns, prob = q))
ixs = []
for i in range(close.size - 1):
if abs(close.ix[i+1] - close.ix[i])/(close.ix[i]) > 0.09451776:
ixs.append(i)
plt.figure()
plt.plot(np.array(close), color = 'grey')
plt.scatter(np.array(ixs), np.array(close.ix[ixs]), color = 'black', marker = 'o')
plt.figure()
plt.plot(np.linspace(0,2280,2280), np.repeat(0, 2280))
plt.scatter(np.array(ixs), np.repeat(0, len(ixs)), color = 'black', marker = 'o')
###############################################################################
def template_pattern(ts, start, end):
vec = ts[start:end]
vec_st = (vec - np.mean(vec))/np.std(vec)
app = np.polyfit(np.linspace(0, vec_st.size, vec_st.size), vec_st, 3)
return app
###############################################################################
def find_pattern(ts, tmp, window):
deriv = np.array([3*tmp[0], 2*tmp[1], tmp[2]])
dpoly = np.poly1d(deriv)
fp = []
errs = []
for i in range(ts.size - window):
loc = ts[i:(i+window)]
loc_st = (loc - np.mean(loc))/np.std(loc)
loctmp = np.polyfit(np.linspace(0,loc_st.size,loc_st.size),loc_st,3)
dloctmp = np.array([3*loctmp[0], 2*loctmp[1], loctmp[2]])
locpoly = np.poly1d(dloctmp)
errs.append(np.sqrt(np.mean((locpoly - dpoly)**2)))
if np.sqrt(np.mean((locpoly - dpoly)**2)) <= 1e-2:
print('pattern found')
fp.append(i)
print('{} patterns found'.format(len(fp)))
return fp, errs
###############################################################################
def approximating_polynomial(x, yhat):
xx = 0
for y in yhat[:(yhat.size-1)]:
deg = yhat.size - (yhat.tolist().index(y) + 1)
xx += (x**deg) * y
return xx + yhat[-1]
###############################################################################
tmp = template_pattern(close, 1240, 1300)
Ps, Es = find_pattern(close, tmp, 100)
plt.figure()
plt.plot(np.array(close), color = 'purple')
plt.scatter(np.array(Ps), np.array(close.ix[Ps]), color = 'black', marker = 'o')
gr = np.array(close)[1240:1300]
stgr = (gr - np.mean(gr))/(np.std(gr))
plt.figure()
plt.plot(stgr, color = 'magenta')
plt.plot(np.linspace(0, 60, 60), approximating_polynomial(np.linspace(0, 60, 60),tmp))
plt.figure()
plt.hist(np.array(Es))
ws = [20,40,60,80,100,200]
for w in ws:
tmp = template_pattern(close, 1240, 1300)
Ps, Es = find_pattern(close, tmp, w)
print(scipy.stats.skew(np.array(Es)))
print(scipy.stats.kurtosis(np.array(Es)))
print('#################################################################')
##############################
mc = []
vc = []
for i in range(1, close.size, 1):
mc.append(np.mean(close.ix[:i]))
vc.append(np.var(close.ix[:i]))
plt.figure()
plt.plot(np.array(mc))
plt.figure()
plt.plot(np.sqrt(np.array(vc)))
#############################
statsmodels.api.tsa.stattools.arma_order_select_ic(np.array(returns),max_ar=5, max_ma=5, ic=['aic', 'bic'], trend='nc')
#method = [‘css-mle’,’mle’,’css’]
mod = statsmodels.api.tsa.ARMA(np.array(returns), order=(4,2)).fit(merthod = 'css',full_output = True)
mod.forecast(steps = 8)
print(mod.params)
print(mod.resid)
phat = mod.predict(0, 2276)
plt.figure()
plt.plot(returns[:500])
plt.plot(phat[:500], color = 'red')
res = returns - phat
np.mean(res)
np.std(res)
sam = statsmodels.api.tsa.arma_generate_sample(ar = 1, ma = 3, nsample = 250)
statsmodels.api.tsa.adfuller(returns) ### rejects the null hypothesis that there is a unit root (i.e. proces is
### NON stationary) => returns are stationary.