def demo3():
close = get_tick('AAL')
frac_df = frac_diff_FFD(close.to_frame(), 0.5)
vol = get_daily_vol(close)
events = cusum_filter(close, 2 * vol)
t1 = get_t1(close, events, num_days=5)
sampled = get_3barriers(close,
events,
ptsl=2,
trgt=vol,
min_ret=0,
num_threads=12,
t1=t1,
side=None)
data = sampled.dropna()
print(data)
features_df = frac_df.loc[data.index].dropna()
features = features_df.values
# get the labels of these events
label = data['t1_type'].loc[features_df.index].values
clf = RandomForestClassifier()
# learn on these features and labels
clf.fit(features, label)
# predict the features (on the same data so overfitting could be an issue)
print(clf.predict(features))
def demo3():
close = get_tick('AAL')
vol = get_daily_vol(close)
sampled_idx = cusum_filter(close, vol)
t1 = get_t1(close, sampled_idx, num_days=7)
side = macd_side(close)
events = get_3barriers(close,
t_events=sampled_idx,
trgt=vol,
ptsl=[1, 2],
t1=t1,
side=side)
events = events.dropna()
bins = get_bins(events, close)
clf = RandomForestClassifier()
x = np.hstack([
events['side'].values[:, np.newaxis],
close.loc[events.index].values[:, np.newaxis]
]) # action and px
# if return was positive, bins = 1
y = bins['bin'].values # supervised answer
clf.fit(x, y)
predicted_probs = np.array([x[1] for x in clf.predict_proba(x)])
# get_signal(events.drop(columns=['side']), 0.2, predicted_probs, events['side'], 2, 1)
get_signal(events.drop(columns=['side']), 0.2, predicted_probs,
events['side'], 2, 12)
def demo_44():
close = get_tick('AAL')
vol = get_daily_vol(close)
sampled_idx = cusum_filter(close, vol)
t1 = get_t1(close, sampled_idx, num_days=1)
trgt = vol
events = get_3barriers(close,
t_events=sampled_idx,
trgt=trgt,
ptsl=1,
t1=t1)
print(events.head())
num_threads = 24
num_co_events = mp_pandas_obj(get_num_co_events,
('molecule', events.index),
num_threads,
close_idx=close.index,
t1=events['t1'])
num_co_events = num_co_events.loc[~num_co_events.index.duplicated(
keep='last')]
num_co_events = num_co_events.reindex(close.index).fillna(0)
num_threads = 24
tw = mp_pandas_obj(get_sample_tw, ('molecule', events.index),
num_threads,
t1=events['t1'],
num_co_events=num_co_events)
exp_decay = get_time_decay(tw, last_w=.1, is_exp=True)
print(exp_decay.head())
def demo2():
df = get_google_all()
df.index = pd.DatetimeIndex(df['Date'].values)
close = df["Close"]
vol = get_daily_vol(close)
sampled_idx = cusum_filter(close, vol)
t1 = get_t1(close, sampled_idx, num_days=1)
side = None
events = get_3barriers(close, t_events=sampled_idx, trgt=vol,ptsl=1, t1=t1, side=side)
index = events.index
features_df = df.drop(columns=["Date"]).dropna().loc[index]
features = features_df
label = events['t1_type'].loc[features_df.index]
clf = RandomForestClassifier()
t1_ = t1.loc[features.index]
# No purge, with embargo
scores = []
for _ in range(10):
scores_ = cv_score(clf, features, label, pct_embargo=0.01, t1=t1_, purging=False)
scores.append(np.mean(scores_))
print(np.mean(scores), np.var(scores))
# no purge without embargo
scores = []
for _ in range(10):
scores_ = cv_score(clf, features, label, pct_embargo=0., t1=t1_, purging=False)
scores.append(np.mean(scores_))
print(np.mean(scores), np.var(scores))
n_co_events = get_num_co_events(close.index, t1, events.index)
sample_weight = get_sample_tw(t1, n_co_events, events.index)
# no purge with embargo and sample weights added to samples
scores = []
for _ in range(10):
scores_ = cv_score(clf, features, label, sample_weight=sample_weight, pct_embargo=0.01, t1=t1_, purging=False)
scores.append(np.mean(scores_))
print(np.mean(scores), np.var(scores))
# no purge without embargo and sample weights added to samples
scores = []
for _ in range(10):
scores_ = cv_score(clf, features, label, sample_weight=sample_weight, pct_embargo=0., t1=t1_, purging=False)
scores.append(np.mean(scores_))
print(np.mean(scores), np.var(scores))
def demo_42():
close = get_tick('AAL')
vol = get_daily_vol(close)
sampled_idx = cusum_filter(close, vol)
t1 = get_t1(close, sampled_idx, num_days=5)
trgt = vol
events = get_3barriers(close,
t_events=sampled_idx,
trgt=trgt,
ptsl=1,
t1=t1)
print(events.head())
ind_m = get_ind_matrix(close.index, events['t1'])
avg_uniq = get_avg_uniq(ind_m)
print(avg_uniq.head())
phi = seq_bootstrap(ind_m)
print(phi)
def demo():
close = get_tick('AAL')
vol = get_daily_vol(close)
sampled_idx = cusum_filter(close, vol)
t1 = get_t1(close, sampled_idx, num_days=5)
trgt = vol
events = get_3barriers(close,
t_events=sampled_idx,
trgt=trgt,
ptsl=1,
t1=t1)
print(events.head())
num_threads = 1
num_co_events = mp_pandas_obj(get_num_co_events,
('molecule', events.index),
num_threads,
close_idx=close.index,
t1=events['t1'])
fig, ax1 = plt.subplots(figsize=(16, 8))
ax1.set_xlabel('time (s)')
ax1.set_ylabel('num_co_events', color='red')
ax1.plot(num_co_events, color='red')
ax1.tick_params(axis='y', labelcolor='red')
ax2 = ax1.twinx() # instantiate a second axes that shares the same x-axis
ax2.set_ylabel('volatility',
color='blue') # we already handled the x-label with ax1
ax2.plot(vol, color='blue')
ax2.tick_params(axis='y', labelcolor='blue')
fig.tight_layout() # otherwise the right y-label is slightly clipped
plt.savefig(PNG_PATH + "num_co_events.png")
plt.close()
fig, ax1 = plt.subplots(figsize=(16, 8))
ax1.set_xlabel('time')
ax1.set_ylabel('num_co_events', color='red')
ax1.scatter(num_co_events.index, num_co_events.values, color='red')
ax2 = ax1.twinx()
ret = close.pct_change().dropna()
ax2.set_ylabel('return', color='blue')
ax2.scatter(ret.index, ret.values, color='blue')
plt.savefig(PNG_PATH + "num_co_events_scatter.png")
plt.close()
def demo():
# close = get_tick('AAL')
df = get_google_all()
df.index = pd.DatetimeIndex(df['Date'].values)
close = df["Close"]
embg_times = get_embargo_times(close.index, pct_embargo=0.01)
print(embg_times.head())
vol = get_daily_vol(close)
sampled_idx = cusum_filter(close, vol)
t1 = get_t1(close, sampled_idx, num_days=1)
side = None
# events = get_3barriers(close, t_events=sampled_idx, trgt=vol,ptsl=[1, 2], t1=t1, side=side)
events = get_3barriers(close, t_events=sampled_idx, trgt=vol,ptsl=1, t1=t1, side=side)
print(events.head())
index = events.index
features_df = df.drop(columns=["Date"]).dropna().loc[index]
features = features_df
label = events['t1_type'].loc[features_df.index]
# without shuffling
scores = []
for _ in range(10):
clf = RandomForestClassifier()
kfold = KFold(n_splits=10, shuffle=False)
scores.append(cross_val_score(clf, features, label, cv=kfold))
print(np.mean(scores), np.var(scores))
# with shuffling the data before putting into batches
# Shffuling data introduces data leakage because of simlarity among neighborg,
# If you shuffle data uniformly, training data has more information that overlaps test data.
scores = []
for _ in range(10):
clf = RandomForestClassifier()
kfold = KFold(n_splits=10, shuffle=True)
scores.append(cross_val_score(clf, features, label, cv=kfold))
print(np.mean(scores), np.var(scores))