def compute_best_positions_for_num_legs(num_legs, options, distribution,
allow_uncapped_risk, num_to_return):
best_positions = []
option_combos = list(itertools.combinations(options, num_legs))
direction_combos = list(
itertools.product(['Long', 'Short'], repeat=num_legs))
for option_combo in option_combos:
for direction_combo in direction_combos:
option_position = []
for i in range(0, num_legs):
leg = {
'option': option_combo[i],
'direction': direction_combo[i]
}
option_position.append(leg)
if is_position_allowed(option_position, allow_uncapped_risk):
metrics = option_position_metrics.compute_position_metrics(
option_position, distribution)
option_position_results = OptionPositionResults(
option_position, metrics)
update_best_positions_list(option_position_results,
best_positions, num_to_return)
return best_positions
def compute_best_positions_for_num_legs(num_legs, options, distribution, allow_uncapped_risk, num_to_return):
best_positions = []
option_combos = list(itertools.combinations(options, num_legs))
direction_combos = list(itertools.product(['Long', 'Short'], repeat=num_legs))
for option_combo in option_combos:
for direction_combo in direction_combos:
option_position = []
for i in range(0, num_legs):
leg = {
'option': option_combo[i],
'direction': direction_combo[i]
}
option_position.append(leg)
if is_position_allowed(option_position, allow_uncapped_risk):
metrics = option_position_metrics.compute_position_metrics(option_position, distribution)
option_position_results = OptionPositionResults(option_position, metrics)
update_best_positions_list(option_position_results, best_positions, num_to_return)
return best_positions
def test_compute_position_metrics(self):
leg1 = {
'option': {
'type': 'Call',
'strike': 100,
'price': 5
},
'direction': 'Long'
}
option_position = [leg1]
cdf = lambda x: (x - 95) / (115 - 95)
pdf = lambda x: 1 / (115 - 95)
my_distribution = distribution.Distribution(95, 115, cdf, pdf)
actual_result = option_position_metrics.compute_position_metrics(
option_position, my_distribution)
expected_win_pct = 0.5
expected_rr = 2.5 / 1.875
expected_max_loss = -5.0
expected_ev = expected_win_pct * expected_rr - (1 - expected_win_pct)
self.assertAlmostEqual(expected_win_pct, actual_result['win_pct'], 4)
self.assertAlmostEqual(expected_max_loss, actual_result['max_loss'], 4)
self.assertAlmostEqual(expected_rr, actual_result['rr'], 4)
self.assertAlmostEqual(expected_ev, actual_result['ev'], 4)
def test_compute_position_metrics(self):
leg1 = {
'option': {
'type': 'Call',
'strike': 100,
'price': 5
},
'direction': 'Long'
}
option_position = [
leg1
]
cdf = lambda x: (x - 95) / (115 - 95)
pdf = lambda x: 1 / (115 - 95)
my_distribution = distribution.Distribution(95, 115, cdf, pdf)
actual_result = option_position_metrics.compute_position_metrics(option_position, my_distribution)
expected_win_pct = 0.5
expected_rr = 2.5 / 1.875
expected_max_loss = -5.0
expected_ev = expected_win_pct * expected_rr - (1 - expected_win_pct)
self.assertAlmostEqual(expected_win_pct, actual_result['win_pct'], 4)
self.assertAlmostEqual(expected_max_loss, actual_result['max_loss'], 4)
self.assertAlmostEqual(expected_rr, actual_result['rr'], 4)
self.assertAlmostEqual(expected_ev, actual_result['ev'], 4)
