def get_indicator_rr_table(**kwargs):
trade_data = kwargs['trade_data']
indicator_name = kwargs['indicator_name']
strategy_class = kwargs['strategy_class']
long_pnl_field = kwargs['long_pnl_field']
short_pnl_field = kwargs['short_pnl_field']
if 'num_buckets' in kwargs.keys():
num_buckets = kwargs['num_buckets']
else:
num_buckets = 9
trade_data = trade_data[
(np.isfinite(trade_data[long_pnl_field].values.astype(np.float64)))
& (np.isfinite(trade_data[short_pnl_field].values.astype(np.float64)))
& (np.isfinite(trade_data[indicator_name].values.astype(np.float64)))]
signal_correlation = sigut.get_signal_correlation(
strategy_class=strategy_class, signal_name=indicator_name)
ascending_q = True if signal_correlation < 0 else False
bucket_data_output = su.bucket_data(data_input=trade_data,
bucket_var=indicator_name,
num_buckets=num_buckets,
ascending_q=ascending_q)
bucket_data_list = bucket_data_output['bucket_data_list']
bucket_limits = bucket_data_output['bucket_limits']
mean_pnl_list = []
reward_risk_list = []
for i in range(num_buckets):
bucket_data = bucket_data_list[i]
if i <= (num_buckets / 2):
signed_pnl = bucket_data[long_pnl_field]
else:
signed_pnl = bucket_data[short_pnl_field]
stats_output = get_summary_stats(signed_pnl.values.astype(np.float64))
mean_pnl_list.append(stats_output['mean_pnl'])
reward_risk_list.append(stats_output['reward_risk'])
if ascending_q:
indicator_ulimit = np.append(bucket_limits, np.NAN)
else:
indicator_ulimit = np.append(np.NAN, bucket_limits)
return pd.DataFrame.from_items([('indicator_ulimit', indicator_ulimit),
('mean_pnl', mean_pnl_list),
('reward_risk', reward_risk_list)])
def get_indicator_rr_table(**kwargs):
trade_data = kwargs['trade_data']
indicator_name = kwargs['indicator_name']
strategy_class = kwargs['strategy_class']
long_pnl_field = kwargs['long_pnl_field']
short_pnl_field = kwargs['short_pnl_field']
if 'num_buckets' in kwargs.keys():
num_buckets = kwargs['num_buckets']
else:
num_buckets = 9
trade_data = trade_data[(np.isfinite(trade_data[long_pnl_field])) & (np.isfinite(trade_data[short_pnl_field]))]
signal_correlation = sigut.get_signal_correlation(strategy_class=strategy_class,signal_name=indicator_name)
ascending_q = True if signal_correlation<0 else False
bucket_data_output = su.bucket_data(data_input=trade_data, bucket_var=indicator_name, num_buckets=num_buckets,ascending_q=ascending_q)
bucket_data_list = bucket_data_output['bucket_data_list']
bucket_limits = bucket_data_output['bucket_limits']
mean_pnl_list = []
reward_risk_list = []
for i in range(num_buckets):
bucket_data = bucket_data_list[i]
if i <= (num_buckets/2):
signed_pnl = bucket_data[long_pnl_field]
else:
signed_pnl = bucket_data[short_pnl_field]
stats_output = get_summary_stats(signed_pnl.values)
mean_pnl_list.append(stats_output['mean_pnl'])
reward_risk_list.append(stats_output['reward_risk'])
if ascending_q:
indicator_ulimit = np.append(bucket_limits, np.NAN)
else:
indicator_ulimit = np.append(np.NAN,bucket_limits)
return pd.DataFrame.from_items([('indicator_ulimit', indicator_ulimit),
('mean_pnl', mean_pnl_list),
('reward_risk',reward_risk_list)])
def get_indicator_rr_double_table(**kwargs):
trade_data = kwargs['trade_data']
indicator_list = kwargs['indicator_list']
strategy_class = kwargs['strategy_class']
long_pnl_field = kwargs['long_pnl_field']
short_pnl_field = kwargs['short_pnl_field']
#print(indicator_list)
if 'num_buckets' in kwargs.keys():
num_buckets = kwargs['num_buckets']
else:
num_buckets = 3
trade_data = trade_data[
(np.isfinite(trade_data[long_pnl_field].values.astype(np.float64)))
& (np.isfinite(trade_data[short_pnl_field].values.astype(np.float64)))
&
(np.isfinite(trade_data[indicator_list[0]].values.astype(np.float64)))
&
(np.isfinite(trade_data[indicator_list[1]].values.astype(np.float64)))]
signal_correlation1 = sigut.get_signal_correlation(
strategy_class=strategy_class, signal_name=indicator_list[0])
signal_correlation2 = sigut.get_signal_correlation(
strategy_class=strategy_class, signal_name=indicator_list[1])
ascending_q1 = True if signal_correlation1 < 0 else False
ascending_q2 = True if signal_correlation2 < 0 else False
bucket_data_output = su.bucket_data(data_input=trade_data,
bucket_var=indicator_list[0],
num_buckets=num_buckets,
ascending_q=ascending_q1)
bucket_data_list1 = bucket_data_output['bucket_data_list']
if ascending_q1:
bucket_limits1_full = np.repeat(
np.append(bucket_data_output['bucket_limits'], np.NAN),
num_buckets)
else:
bucket_limits1_full = np.repeat(
np.append(np.NAN, bucket_data_output['bucket_limits']),
num_buckets)
bucket_limits2_full = np.empty([1, 0])
mean_pnl_list = []
reward_risk_list = []
for i in range(len(bucket_data_list1)):
bucket_data_output = su.bucket_data(data_input=bucket_data_list1[i],
bucket_var=indicator_list[1],
num_buckets=num_buckets,
ascending_q=ascending_q2)
bucket_data_list2 = bucket_data_output['bucket_data_list']
bucket_limits2 = bucket_data_output['bucket_limits']
if ascending_q2:
bucket_limits2_full = np.append(bucket_limits2_full,
np.append(bucket_limits2, np.NAN))
else:
bucket_limits2_full = np.append(bucket_limits2_full,
np.append(np.NAN, bucket_limits2))
for j in range(len(bucket_data_list2)):
if i <= (num_buckets / 2):
signed_pnl = bucket_data_list2[j][long_pnl_field]
else:
signed_pnl = bucket_data_list2[j][short_pnl_field]
stats_output = get_summary_stats(signed_pnl.values)
mean_pnl_list.append(stats_output['mean_pnl'])
reward_risk_list.append(stats_output['reward_risk'])
return pd.DataFrame.from_items([('indicator1_ulimit', bucket_limits1_full),
('indicator2_ulimit', bucket_limits2_full),
('mean_pnl', mean_pnl_list),
('reward_risk', reward_risk_list)])
def get_indicator_rr_double_table(**kwargs):
trade_data = kwargs['trade_data']
indicator_list = kwargs['indicator_list']
strategy_class = kwargs['strategy_class']
long_pnl_field = kwargs['long_pnl_field']
short_pnl_field = kwargs['short_pnl_field']
if 'num_buckets' in kwargs.keys():
num_buckets = kwargs['num_buckets']
else:
num_buckets = 3
trade_data = trade_data[(np.isfinite(trade_data[long_pnl_field])) & (np.isfinite(trade_data[short_pnl_field]))]
signal_correlation1 = sigut.get_signal_correlation(strategy_class=strategy_class,signal_name=indicator_list[0])
signal_correlation2 = sigut.get_signal_correlation(strategy_class=strategy_class,signal_name=indicator_list[1])
ascending_q1 = True if signal_correlation1<0 else False
ascending_q2 = True if signal_correlation2<0 else False
bucket_data_output = su.bucket_data(data_input=trade_data, bucket_var=indicator_list[0],
num_buckets=num_buckets, ascending_q=ascending_q1)
bucket_data_list1 = bucket_data_output['bucket_data_list']
if ascending_q1:
bucket_limits1_full = np.repeat(np.append(bucket_data_output['bucket_limits'], np.NAN), num_buckets)
else:
bucket_limits1_full = np.repeat(np.append(np.NAN,bucket_data_output['bucket_limits']), num_buckets)
bucket_limits2_full = np.empty([1, 0])
mean_pnl_list = []
reward_risk_list = []
for i in range(len(bucket_data_list1)):
bucket_data_output = su.bucket_data(data_input=bucket_data_list1[i],
bucket_var=indicator_list[1],
num_buckets=num_buckets,
ascending_q=ascending_q2)
bucket_data_list2 = bucket_data_output['bucket_data_list']
bucket_limits2 = bucket_data_output['bucket_limits']
if ascending_q2:
bucket_limits2_full = np.append(bucket_limits2_full,
np.append(bucket_limits2, np.NAN))
else:
bucket_limits2_full = np.append(bucket_limits2_full,
np.append(np.NAN,bucket_limits2))
for j in range(len(bucket_data_list2)):
if i <= (num_buckets/2):
signed_pnl = bucket_data_list2[j][long_pnl_field]
else:
signed_pnl = bucket_data_list2[j][short_pnl_field]
stats_output = get_summary_stats(signed_pnl.values)
mean_pnl_list.append(stats_output['mean_pnl'])
reward_risk_list.append(stats_output['reward_risk'])
return pd.DataFrame.from_items([('indicator1_ulimit', bucket_limits1_full),
('indicator2_ulimit', bucket_limits2_full),
('mean_pnl', mean_pnl_list),
('reward_risk',reward_risk_list)])
