/clustering_forecasts.py Secret
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May 12, 2023 13:05
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clustering forecasts
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import matplotlib | |
matplotlib.use("TkAgg") | |
from syscore.constants import arg_not_supplied | |
from systems.basesystem import System | |
from systems.provided.rob_system.run_system import futures_system | |
from sysquant.estimators.clustering_correlations import * | |
import pandas as pd | |
system = futures_system() | |
instrument_list = system.get_instrument_list() | |
all_pandl = dict() | |
instrument_weights = system.config.instrument_weights | |
for rule_name in list(system.rules.trading_rules().keys()): | |
pandl_this_rule = dict() | |
for instrument_code in list(instrument_weights.keys()): | |
pandl_this_code_rule = system.accounts.pandl_for_instrument_forecast( | |
instrument_code, rule_name | |
).percent.gross | |
pandl_this_code_rule = ( | |
pandl_this_code_rule * instrument_weights[instrument_code] | |
) | |
pandl_this_rule[instrument_code] = pandl_this_code_rule | |
pandl_this_rule = pd.concat(pandl_this_rule, axis=1) | |
pandl_this_rule = pandl_this_rule.sum(axis=1) | |
all_pandl[rule_name] = pandl_this_rule | |
all_pandl = pd.concat(all_pandl, axis=1) | |
import pickle | |
f = open("/home/rob/temp.pck", "wb") | |
pickle.dump(all_pandl, f) | |
f.close() | |
from sysquant.estimators.correlation_estimator import correlationEstimate | |
corr_matrix = correlationEstimate.from_pd(all_pandl.corr()) | |
def display_clusters(system, clusters): | |
for idx, cluster in enumerate(clusters): | |
print("Cluster %d" % (idx + 1)) | |
print(cluster) | |
def show_cluster(system, cluster): | |
instruments = cluster | |
assets = [system.data.asset_class_for_instrument(code) for code in instruments] | |
classes = list(set(assets)) | |
for aclass in classes: | |
subset = [ | |
code | |
for (code, asset_class) in zip(instruments, assets) | |
if asset_class == aclass | |
] | |
print("%s: %s" % (aclass, str(subset))) | |
N = 12 | |
clusters = cluster_correlation_matrix(corr_matrix, N) | |
display_clusters(system, clusters) | |
## get turnovers | |
## doesn't matter which instrument as long as long history | |
turnovers = dict() | |
for rule_name in corr_matrix.columns: | |
turnovers[rule_name] = system.accounts.forecast_turnover("SP500_micro", rule_name) | |
print({k: v for k, v in sorted(turnovers.items(), key=lambda item: item[1])}) |
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