def test_best_ranking_dtype_is_int8(self):
sm = SwarmManager()
index = ['0', '1', '2', '3', '4', '5']
ranks = [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan]
rnk = pd.Series(ranks, index=index)
r = sm._get_nbest(rnk, nsystems=2)
self.assertEqual(r.dtype, np.int8)
def test_best_ranking_full_nans(self):
sm = SwarmManager()
index = ['0', '1', '2', '3', '4', '5']
ranks = [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan]
rnk = pd.Series(ranks, index=index)
r = sm._get_nbest(rnk, nsystems=2)
# Indexes is a string, because these are pd.Series indexes not array int32 index
self.assertEqual(r['0'], 0)
self.assertEqual(r['1'], 0)
self.assertEqual(r['2'], 0)
self.assertEqual(r['3'], 0)
self.assertEqual(r['4'], 0)
self.assertEqual(r['5'], 0)
def test_best_ranking_negative_and_zeros_filter(self):
sm = SwarmManager()
index = ['0', '1', '2', '3', '4', '5']
ranks = [np.nan, 0, -5, np.nan, 9, np.nan]
rnk = pd.Series(ranks, index=index)
r = sm._get_nbest(rnk, nsystems=2)
# Indexes is a string, because these are pd.Series indexes not array int32 index
self.assertEqual(r['0'], 0)
self.assertEqual(r['1'], 0)
self.assertEqual(r['2'], 0)
self.assertEqual(r['3'], 0)
self.assertEqual(r['4'], 1)
self.assertEqual(r['5'], 0)
self.assertEqual(r.dtype, np.int8)
'ignore_eqty_with_negative_ma_slope_period':
5, # Slope lookback filter = MA-MA[-slope_lookback] <= 0
# Ignoring all swarm members when the change of AvgSwarm equity is negative
'ignore_if_avg_swarm_negative_change':
True, # Comment the line to turn off
'ignore_if_avg_swarm_negative_change_period':
14, # AvgSwarm change period
},
'rebalance_time_function': SwarmRebalance.every_monday,
'global_filter_function': SwarmFilter.swingpoint_threshold,
'global_filter_params': {
'up_factor': 10.0,
'down_factor': 10.0,
'period': 5,
},
},
'costs': {
'manager': CostsManagerEXOFixed,
'context': {
'costs_options': 3.0,
'costs_futures': 3.0,
}
}
}
swarm_manager = SwarmManager(STRATEGY_CONTEXT)
swarm_manager.run_swarm()
picked_swarm = swarm_manager.pick()
picked_swarm
from backtester.swarms.manager import SwarmManager
from backtester.exoinfo import EXOInfo
import pandas as pd
import numpy as np
import scipy
import glob
from pandas.tseries.offsets import *
for file in glob.glob("./temp_swarms/*.swm"):
# set file name here
# Real swarm from .swm
df = SwarmManager.load(file)
df = df.swarm
print('calculating portfolio for ---', file)
# ## Systems overall performance estimation
# In[6]:
temp_l = []
for strat in df.columns:
input_strategy_data = df[strat] # This data is used in metrics calc
price_change = input_strategy_data.diff()
max_dd = (input_strategy_data -
from strategies.strategy_swingpoint import StrategySwingPoint
from strategies.strategy_macross_with_trail import StrategyMACrossTrail
try:
from .settings import *
except SystemError:
from settings import *
if __name__ == '__main__':
for f in os.listdir(sys.argv[1]):
if 'strategy_' not in f:
continue
exo_name = os.path.join(sys.argv[1], f)
print("Processing " + exo_name)
for name, swarm_context in BATCH_CONTEXT.items():
print("Running swarm " + name)
strategy_context = STRATEGY_CONTEXT_COMMON.copy()
strategy_context.update(swarm_context)
strategy_context['strategy']['exo_name'] = exo_name
strategy_context['strategy']['suffix'] = name
smgr = SwarmManager(strategy_context)
smgr.run_swarm()
smgr.pick()
# Saving results to swarms directory
smgr.save('./swarms/')
print('Done')