Hello,
Please find what i'm using when I have to find pairs (its only a part of the code that permits to get clusters, you just have to refine a bit after this step to get cointegrated pairs by applying ad-fuller test on each pairs inside of clusters for example)
PCA_COMPONENTS = 30
EPS = 1.5
MIN_SAMPLES = 2.
CLSTR_SIZE_MIN = 5
CLSTR_SIZE_LIMIT = 9999
LOOKBACK_PERIOD = 250
COMPUTE_PERIOD = 50
def initialize(context):
"""
Called once at the start of the algorithm.
"""
set_benchmark(symbol('SPY'))
#set_commission(commission.PerShare(cost=0.000, min_trade_cost=0))
#set_slippage(slippage.FixedSlippage(spread=0))
monthly_top_volume = (
AverageDollarVolume(window_length=200)
.top(1000, mask=QTradableStocksUS())
.downsample('week_start')
)
universe = QTradableStocksUS() #& monthly_top_volume
close_prices = USEquityPricing.close.latest
beta = 0.66*RollingLinearRegressionOfReturns(
target=sid(8554),
returns_length=5,
regression_length=260,
mask=universe
).beta + 0.33*1.0
pipe = Pipeline(
columns={
'close_prices':close_prices,
'sector': Sector(),
'beta': beta,
},
screen=(universe),
)
# Create our dynamic stock selector.
algo.attach_pipeline(pipe, 'pipe')
# Update clusters once a month
schedule_function(
compute_clusters,
date_rules.month_end(),
time_rules.market_close(minutes=30),
)
# Rebalance every day, 1 hour after market open.
schedule_function(
rebalance,
date_rules.every_day(),
time_rules.market_close(minutes=30),
)
# Record any custom data at the end of each day
schedule_function(record_data,
date_rules.every_day(),
time_rules.market_close())
def get_cluster(prices):
returns = prices.pct_change()
returns = returns.iloc[1:,:].dropna(axis=1)
pca = PCA(n_components=PCA_COMPONENTS)
pca.fit(returns)
X = pca.components_.T
#X = np.hstack(
#(pca.components_.T,
# MktCap[returns.columns].values[:, np.newaxis],)
#)
X = preprocessing.StandardScaler().fit_transform(X)
clf = DBSCAN(eps=EPS, min_samples=MIN_SAMPLES)
clf.fit(X)
labels = clf.labels_
n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)
clustered = clf.labels_
clustered_series = pd.Series(index=returns.columns, data=clustered.flatten())
clustered_series = clustered_series[clustered_series != -1]
nb_clusters = clustered_series.value_counts()
ticker_count_reduced = nb_clusters[(nb_clusters>=CLSTR_SIZE_MIN) & (nb_clusters<=CLSTR_SIZE_LIMIT)]
return clustered_series[clustered_series.isin(ticker_count_reduced.index)==True]
def compute_clusters(context, data):
# Get the alpha factor data from the pipeline output
context.pipeline_data = algo.pipeline_output('pipe')
context.securities = context.pipeline_data.index.tolist()
prices = data.history(context.pipeline_data.index, 'price', bar_count=LOOKBACK_PERIOD, frequency='1d')
context.clusters = get_cluster(prices)
def rebalance(context, data):
try:
context.clusters
except:
compute_clusters(context, data)
# Get the alpha factor data from the pipeline output
context.pipeline_data = algo.pipeline_output('pipe')
context.securities = context.pipeline_data.index.tolist()
context.sector_betas = context.pipeline_data.sector
prices = data.history(context.pipeline_data.index, 'price', bar_count=COMPUTE_PERIOD, frequency='1d')
for s in context.clusters.index:
if s not in prices.columns:
context.clusters.drop(s, inplace=True)
# Continue the code...
