def opt_func_base(stock_name, start_day_index, end_day_index, X_list):
if len(X_list.shape) == 2:
X_list = X_list[0]
optimize_result = OptimizeResult()
cache_filename = get_cache_filename(stock_name, start_day_index, end_day_index)
if os.path.isfile(cache_filename):
optimize_result.load(cache_filename)
#print("stock worm cache loaded, size={}".format(optimize_result.get_size()))
else:
print("cannot find file cache:{}, will create new cache.".format(cache_filename))
print("Checking: {}".format(X_list))
hmm_model = HmmModel(stock_name)
total_profit, profit_daily_avg = hmm_model.train(X_list, start_day_index, end_day_index)
if total_profit == -1:
print("Training failed.")
return total_profit
print("Finished, total_profit:{}".format(total_profit))
strategy_features = hmm_model.get_strategy_features()
optimize_result.insert_result(X_list, strategy_features + [total_profit, profit_daily_avg])
optimize_result.save(cache_filename)
return profit_daily_avg
class StockWormManager:
mixed_domain = [
{
'name': 'n_neurons',
'type': 'discrete',
'domain': tuple(range(20, 160, 20))
},
{
'name': 'learning_rate',
'type': 'discrete',
'domain': (0.001, 0.002, 0.003, 0.004)
},
{
'name': 'num_layers',
'type': 'discrete',
'domain': (1, 2, 3, 4, 5, 6, 7, 8)
},
{
'name': 'rnn_type',
'type': 'discrete',
'domain': (0, 1, 2)
},
{
'name': 'learning_period',
'type': 'discrete',
'domain': (20, 30, 40, 50)
},
{
'name': 'prediction_period',
'type': 'discrete',
'domain': (5, 10, 20)
},
{
'name': 'n_repeats',
'type': 'discrete',
'domain': (1, 3, 5, 10, 20, 30, 40)
},
{
'name': 'beta',
'type': 'discrete',
'domain': (99, 98)
},
{
'name': 'ema',
'type': 'discrete',
'domain': (1, 10, 20)
},
{
'name': 'time_format',
'type': 'discrete',
'domain': (0, 1, 2)
}, #1 for stepofday, 2 for stepofweek
{
'name': 'volume_input',
'type': 'discrete',
'domain': (0, 1)
},
{
'name': 'use_centralized_bid',
'type': 'discrete',
'domain': (0, 1)
},
{
'name': 'split_daily_data',
'type': 'discrete',
'domain': (0, 1)
},
{
'name': 'is_stateful',
'type': 'discrete',
'domain': (0, 1)
},
{
'name': 'ref_stock_id',
'type': 'discrete',
'domain': (-1, 992, 3524, 139301, 160271)
},
]
mixed_domain_test = [
{
'name': 'n_neurons',
'type': 'discrete',
'domain': tuple(range(20, 160, 20))
},
{
'name': 'learning_rate',
'type': 'discrete',
'domain': (0.001, 0.002, 0.003, 0.004)
},
{
'name': 'num_layers',
'type': 'discrete',
'domain': (1, 2, 3, 4)
},
{
'name': 'rnn_type',
'type': 'discrete',
'domain': (0, 1, 2)
},
{
'name': 'learning_period',
'type': 'discrete',
'domain': (20, )
},
{
'name': 'prediction_period',
'type': 'discrete',
'domain': (10, )
},
{
'name': 'n_repeats',
'type': 'discrete',
'domain': (1, )
},
{
'name': 'beta',
'type': 'discrete',
'domain': (99, )
},
{
'name': 'ema',
'type': 'discrete',
'domain': (20, )
},
{
'name': 'time_format',
'type': 'discrete',
'domain': (0, 1, 2)
}, #1 for stepofday, 2 for stepofweek
{
'name': 'volume_input',
'type': 'discrete',
'domain': (0, 1)
},
{
'name': 'use_centralized_bid',
'type': 'discrete',
'domain': (0, 1)
},
{
'name': 'split_daily_data',
'type': 'discrete',
'domain': (0, 1)
},
{
'name': 'is_stateful',
'type': 'discrete',
'domain': (0, 1)
},
{
'name': 'ref_stock_id',
'type': 'discrete',
'domain': (-1, 992, 3524, 139301, 160271)
},
]
mixed_domain_future = [
{
'name': 'n_neurons',
'type': 'discrete',
'domain': tuple(range(20, 160, 20))
},
{
'name': 'learning_rate',
'type': 'discrete',
'domain': (0.001, 0.002, 0.003, 0.004)
},
{
'name': 'num_layers',
'type': 'discrete',
'domain': (1, 2, 3, 4, 5, 6, 7, 8)
},
{
'name': 'rnn_type',
'type': 'discrete',
'domain': (0, 1, 2)
},
{
'name': 'learning_period',
'type': 'discrete',
'domain': (20, 30, 40, 50)
},
{
'name': 'prediction_period',
'type': 'discrete',
'domain': (5, 10, 20)
},
{
'name': 'n_repeats',
'type': 'discrete',
'domain': (1, 3, 5, 10, 20, 30)
},
{
'name': 'beta',
'type': 'discrete',
'domain': (99, 98)
},
{
'name': 'ema',
'type': 'discrete',
'domain': (1, 10, 20)
},
{
'name': 'time_format',
'type': 'discrete',
'domain': (0, 1, 2)
}, #1 for stepofday, 2 for stepofweek
{
'name': 'volume_input',
'type': 'discrete',
'domain': (0, 1)
},
{
'name': 'use_centralized_bid',
'type': 'discrete',
'domain': (1, )
},
{
'name': 'split_daily_data',
'type': 'discrete',
'domain': (0, )
},
{
'name': 'is_stateful',
'type': 'discrete',
'domain': (0, 1)
},
{
'name': 'ref_stock_id',
'type': 'discrete',
'domain': (-1, 800005)
},
]
def __init__(self,
stock_name,
stock_data_path,
npy_files_path,
is_future=False,
slippage=0):
self.stock_name = stock_name
self.stock_id = get_stock_id_by_name(stock_name)
if self.stock_id == None:
print("cannot find stock id with name: {}".format(stock_name))
os.exit()
self.stock_data_path = stock_data_path
self.npy_files_path = npy_files_path
self.worm_list = []
self.is_future = is_future
self.slippage = slippage
def search_worms(self,
start_day_index,
end_day_index,
max_iter=300,
is_test=False):
if is_test == True:
mixed_domain = self.mixed_domain_test
else:
mixed_domain = self.mixed_domain
if self.is_future == True:
mixed_domain = self.mixed_domain_future
self.optimize_result = OptimizeResult()
stock_worm_cache_file = self.get_stockworm_cache_file(
start_day_index, end_day_index)
create_if_not_exist(os.path.dirname(stock_worm_cache_file))
if os.path.isfile(stock_worm_cache_file):
self.optimize_result.load(stock_worm_cache_file)
print("stock worm cache loaded, size={}".format(
self.optimize_result.get_size()))
else:
print("cannot find file cache:{}, will create new cache.".format(
stock_worm_cache_file))
self.stock_worm_cache_file = stock_worm_cache_file
opt_func = partial(self.opt_func, start_day_index, end_day_index)
opt_handler = GPyOpt.methods.BayesianOptimization(
f=opt_func, # Objective function
domain=mixed_domain, # Box-constraints of the problem
initial_design_numdata=30, # Number data initial design
acquisition_type='EI', # Expected Improvement
exact_feval=True,
maximize=True) # True evaluations, no sample noise
opt_handler.run_optimization(max_iter, eps=0)
def opt_func(self, start_day, end_day, X_list):
assert (len(X_list) == 1)
features = X_list[0]
ref_stock_id = features[14]
if ref_stock_id == self.stock_id:
ref_stock_id = -1
features[14] = ref_stock_id
print("starting test: {}".format(self.get_parameter_str(features)))
cached_result, index = self.optimize_result.find_result(features)
if cached_result is not None:
print("find from cache. skip...")
else:
stock_worm = StockWorm(self.stock_name,
self.stock_id,
self.npy_files_path,
is_future=self.is_future,
slippage=self.slippage)
if stock_worm.validate(features, start_day, end_day) == False:
print("validate failed for worm: {}".format(
self.get_parameter_str(features)))
return np.array(-1).reshape((1, 1))
total_profit, profit_daily, errors_daily = stock_worm.init(
features, start_day, end_day)
n_days = len(profit_daily)
profit_mean = np.mean(profit_daily)
error_mean = np.mean(errors_daily)
# to do insert strategy features.
strategy_features = list(stock_worm.get_strategy_features())
self.optimize_result.insert_result(
features, strategy_features +
[total_profit, n_days, error_mean, profit_mean])
print("result saved to: {}".format(self.stock_worm_cache_file))
self.optimize_result.save(self.stock_worm_cache_file)
print(
"total_profit:{} in {} days, profit_mean:{} error:{} parameters:{}"
.format(total_profit, n_days, profit_mean, error_mean,
self.get_parameter_str(features)))
return np.array(profit_mean).reshape((1, 1))
def get_swarm_path(self, start_day_index, end_day_index):
return os.path.join(self.stock_data_path,
"{}_{}".format(self.stock_name, self.stock_id),
"{}-{}".format(start_day_index, end_day_index))
def get_stockworm_cache_file(self, start_day_index, end_day_index):
swarm_path = self.get_swarm_path(start_day_index, end_day_index)
return os.path.join(swarm_path, "stockworm_cache.txt")
def update_worms_from_cache(self, n_number, start_day_index,
end_day_index):
optimize_result = OptimizeResult()
stockworm_cache_file = self.get_stockworm_cache_file(
start_day_index, end_day_index)
optimize_result.load(stockworm_cache_file)
top_worms = optimize_result.get_best_results(n_number)
assert (len(top_worms) == n_number)
for i in range(n_number):
features = top_worms[i, :15]
strategy_features = top_worms[i, 15:21]
md5 = top_worms[i, -1]
model_save_path = self.get_model_save_path(start_day_index,
end_day_index, md5)
new_worm = StockWorm(self.stock_name,
self.stock_id,
self.npy_files_path,
model_save_path,
is_future=self.is_future,
slippage=self.slippage)
if os.path.isdir(model_save_path) and new_worm.load() == True:
pass
else:
total_profit, profit_daily, errors_daily = new_worm.init(
features,
start_day_index,
end_day_index,
strategy_features=strategy_features)
new_worm.save()
print("training finished for model {}, total_profit:{}".format(
i, total_profit))
new_worm.report()
testing_total_profit, testing_profit_daily, n_data_appended = new_worm.test(
)
if n_data_appended > 0:
print(
"testing finished for model {}, total_profit:{} in {} days, new data for {} days appended"
.format(i, testing_total_profit, len(testing_profit_daily),
n_data_appended))
new_worm.save()
new_worm.report()
self.worm_list.append(new_worm)
def report(self):
assert (self.worm_list is not None)
for i in range(len(self.worm_list)):
print("Report for Worm No.{}".format(i + 1))
self.worm_list[i].report()
def get_model_save_path(self, start_day_index, end_day_index, md5=None):
swarm_path = self.get_swarm_path(start_day_index, end_day_index)
if md5 is None:
model_save_path = os.path.join(swarm_path, "models")
else:
model_save_path = os.path.join(swarm_path, "models", md5)
return model_save_path
def load(self):
model_save_path = self.get_model_save_path(0, 60)
directories = [
os.path.join(model_save_path, f)
for f in os.listdir(model_save_path)
if os.path.isdir(os.path.join(model_save_path, f))
]
self.worm_list = []
for d in directories:
new_worm = StockWorm(self.stock_id, self.npy_files_path, d)
new_worm.load()
self.worm_list.append(new_worm)
def get_worm(self, n):
assert (len(self.worm_list) > n)
return self.worm_list[n]
def test(self):
profit_avg_list = []
profit_list = []
for worm in self.worm_list:
training_total_profit, \
training_daily_profit, testing_total_profit, \
testing_daily_profit = worm.get_historic_metrics()
testing_len = len(testing_daily_profit)
traning_len = len(training_daily_profit)
all_profit = np.concatenate(
(training_daily_profit, testing_daily_profit), axis=0)
all_profit_cumsum = np.cumsum(all_profit)
profit_avg = all_profit_cumsum / (
np.arange(len(all_profit_cumsum)) + 1)
profit_avg_list.append(profit_avg[traning_len - 1:])
profit_list.append(testing_daily_profit)
assert (len(profit_avg[traning_len -
1:]) == len(testing_daily_profit) + 1)
profit_avg_arr = np.array(profit_avg_list)
profit_arr = np.array(profit_list)
# find the argmax from profit_avg_arr
argmax_arr = np.argmax(profit_avg_arr, axis=0)
overall_profit = profit_arr[argmax_arr[:-1],
np.arange(len(profit_arr[0]))]
print(argmax_arr[:-1])
print(overall_profit)
print("OVERALL RESULTS:")
print(np.prod(overall_profit + 1) - 1)
def plot(self):
pass
def get_parameter_str(self, X):
parameter_str = ""
for i in range(len(self.mixed_domain)):
parameter_str += self.mixed_domain[i]["name"]
parameter_str += ':'
parameter_str += str(X[i])
parameter_str += ','
return parameter_str
import sys
import os.path
sys.path.append("../")
from util import *
from optimizeresult import OptimizeResult
from pathlib import Path
stock_data_path = get_stock_data_dir()
# This is to get the directory that the program
# is currently running in.
for filename in Path(stock_data_path).glob('**/stockworm_cache.txt'):
optimize_result = OptimizeResult(result_column_index=-2)
optimize_result.load(filename)
print(optimize_result.get_n_columns())
if (optimize_result.get_n_columns() == 24):
print("{} has been already migrated.".format(filename))
continue
print('migrating {}'.format(filename))
assert(optimize_result.get_n_columns() == 23)
optimize_result.add_column(14, -1)
assert(optimize_result.get_n_columns() == 24)
optimize_result.save(filename)
class TradeStrategyFactory:
mixed_domain = [{'name': 'buy_threshold', 'type': 'continuous', 'domain': (0, 0.005)},
{'name': 'sell_threshold', 'type': 'continuous', 'domain': (-0.005, 0)},
{'name': 'stop_loss', 'type': 'continuous', 'domain': (-0.02, -0.003)},
{'name': 'stop_gain', 'type': 'continuous', 'domain': (0.003, 0.02)},
{'name': 'skip_at_beginning', 'type': 'discrete', 'domain': (0,5, 10, 20)},
{'name': 'value_ma', 'type': 'discrete', 'domain': (1,3,5,10,20)}
]
def __init__(self, cache_file=None, n_max_trades_per_day=4, slippage=0, courtage=0, is_future=False):
self.n_max_trades_per_day = n_max_trades_per_day
self.slippage = slippage
self.courtage = courtage
self.is_future = is_future
self.trade_strategy = None
# load the initial data file
self.optimize_result = OptimizeResult()
self.cache_file = cache_file
if cache_file is not None:
self.optimize_result.load(cache_file)
return
def create_strategy(self, features):
print("is_future")
print(self.is_future)
if self.is_future:
classTradeStrategy = TradeStrategyFuture
else:
classTradeStrategy = TradeStrategy
return classTradeStrategy(features, self.n_max_trades_per_day,
self.slippage, self.courtage)
def create_from_file(self, filename, n_number):
optimize_result = OptimizeResult(result_column_index=-1)
optimize_result.load(filename)
data = optimize_result.get_best_results(n_number)
trade_strategy_list = []
if self.is_future:
classTradeStrategy = TradeStrategyFuture
else:
classTradeStrategy = TradeStrategy
for i in range(n_number):
X_list = data[i,:4]
trade_strategy_list.append(classTradeStrategy(X_list, self.n_max_trades_per_day,
self.slippage, self.courtage))
return trade_strategy_list
def create_trade_strategies(self, data, iter, max_iter=100):
#assert(data.shape[1]==504)
self.data = data
init_numdata = int(max_iter / 4)
trade_strategy_list = []
self.max_profit = -1
self.trade_strategy = None
for i in range(iter):
print("Searching Strategies, Run: {}".format(i))
self.n_iter = 0
myBopt = GPyOpt.methods.BayesianOptimization(self.get_profit, # Objective function
domain=self.mixed_domain, # Box-constraints of the problem
initial_design_numdata = init_numdata, # Number data initial design
acquisition_type='EI', # Expected Improvement
exact_feval = True,
maximize = True) # True evaluations, no sample noise
myBopt.run_optimization(max_iter, eps=0)
return self.trade_strategy
def get_profit(self, X_list):
assert(len(X_list)==1)
if self.is_future:
classTradeStrategy = TradeStrategyFuture
else:
classTradeStrategy = TradeStrategy
X_list = X_list[0]
self.n_iter += 1
cached_result, index = self.optimize_result.find_result(X_list)
trade_strategy = classTradeStrategy(X_list, n_max_trades_per_day=self.n_max_trades_per_day,
slippage=self.slippage,
courtage=self.courtage)
if cached_result is not None:
print("find cached result: {} for {}".format(cached_result,
trade_strategy.get_parameter_str()))
avg_daily_profit = cached_result[0]
else:
total_profit, daily_profit_list,_ = trade_strategy.get_profit(self.data)
avg_daily_profit = np.mean(daily_profit_list)
self.optimize_result.insert_result(X_list, avg_daily_profit)
if avg_daily_profit > self.max_profit:
print("find new record: {}, {}".format(avg_daily_profit,
trade_strategy.get_parameter_str()))
self.max_profit = avg_daily_profit
self.trade_strategy = trade_strategy
if self.n_iter % 10 == 0:
print("iteration: {}, cachesize={}, avg_daily_profit:{}".format(self.n_iter,
self.optimize_result.get_size(),
avg_daily_profit))
if self.cache_file != None:
self.optimize_result.save(self.cache_file)
return avg_daily_profit.reshape((1,1))
class StockWormManager:
mixed_domain = [
{
'name': 'n_neurons',
'type': 'discrete',
'domain': tuple(range(20, 160, 20))
},
{
'name': 'learning_rate',
'type': 'discrete',
'domain': (0.001, 0.002, 0.003, 0.004)
},
{
'name': 'num_layers',
'type': 'discrete',
'domain': (1, 2, 3, 4, 5, 6, 7, 8)
},
{
'name': 'rnn_type',
'type': 'discrete',
'domain': (0, 1, 2)
},
{
'name': 'learning_period',
'type': 'discrete',
'domain': (20, 30, 40)
},
{
'name': 'prediction_period',
'type': 'discrete',
'domain': (2, 5, 10, 20)
},
{
'name': 'n_repeats',
'type': 'discrete',
'domain': (1, 3, 5, 10, 20, 30, 40)
},
{
'name': 'beta',
'type': 'discrete',
'domain': (99, )
},
{
'name': 'ema',
'type': 'discrete',
'domain': (20, )
},
{
'name': 'time_format',
'type': 'discrete',
'domain': (0, 1, 2)
}, #1 for stepofday, 2 for stepofweek
{
'name': 'volume_input',
'type': 'discrete',
'domain': (0, 1)
},
{
'name': 'use_centralized_bid',
'type': 'discrete',
'domain': (0, 1)
},
{
'name': 'split_daily_data',
'type': 'discrete',
'domain': (1, )
}
]
mixed_domain_test = [
{
'name': 'n_neurons',
'type': 'discrete',
'domain': tuple(range(20, 160, 20))
},
{
'name': 'learning_rate',
'type': 'discrete',
'domain': (0.001, 0.002, 0.003, 0.004)
},
{
'name': 'num_layers',
'type': 'discrete',
'domain': (1, 2, 3, 4)
},
{
'name': 'rnn_type',
'type': 'discrete',
'domain': (0, 1, 2)
},
{
'name': 'learning_period',
'type': 'discrete',
'domain': (20, )
},
{
'name': 'prediction_period',
'type': 'discrete',
'domain': (10, )
},
{
'name': 'n_repeats',
'type': 'discrete',
'domain': (1, )
},
{
'name': 'beta',
'type': 'discrete',
'domain': (99, )
},
{
'name': 'ema',
'type': 'discrete',
'domain': (20, )
},
{
'name': 'time_format',
'type': 'discrete',
'domain': (0, 1, 2)
}, #1 for stepofday, 2 for stepofweek
{
'name': 'volume_input',
'type': 'discrete',
'domain': (0, 1)
},
{
'name': 'use_centralized_bid',
'type': 'discrete',
'domain': (0, 1)
},
{
'name': 'split_daily_data',
'type': 'discrete',
'domain': (0, 1)
}
]
def __init__(self, stock_name, stock_index, model_save_path,
npy_files_path):
self.stock_name = stock_name
self.stock_index = stock_index
self.model_save_path = model_save_path
self.npy_files_path = npy_files_path
self.worm_list = []
def search_worms(self,
start_day_index,
end_day_index,
max_iter=300,
is_test=False,
search_strategy=False):
if is_test == True:
mixed_domain = self.mixed_domain_test
else:
mixed_domain = self.mixed_domain
self.optimize_result = OptimizeResult(result_column_index=15)
stock_worm_cache_file = self.get_stockworm_cache_file(
start_day_index, end_day_index)
if os.path.isfile(stock_worm_cache_file):
self.optimize_result.load(stock_worm_cache_file)
print("stock worm cache loaded, size={}".format(
self.optimize_result.get_size()))
else:
print("cannot find file cache:{}, will create new cache.".format(
stock_worm_cache_file))
self.stock_worm_cache_file = stock_worm_cache_file
strategy_cache_file = self.get_strategy_cache_file(
start_day_index, end_day_index)
trade_strategy_factory = TradeStrategyFactory(
cache_file=strategy_cache_file)
if os.path.isfile(strategy_cache_file) and search_strategy == False:
print("find strategy_cache:{}, loading...".format(
strategy_cache_file))
strategy_list = trade_strategy_factory.create_from_file(
strategy_cache_file, NUM_STRATEGIES)
else:
if search_strategy == True:
print("search_strategy is True, searching strategies again...")
else:
print("cannot find strategy cache:{}, generating...".format(
strategy_cache_file))
data = load_strategy_input_data(self.stock_index, start_day_index,
end_day_index)
# the input data: timestamp, value, and price.
strategy_list = trade_strategy_factory.create_trade_strategies(
data, 5)
opt_func = partial(self.opt_func, strategy_list, start_day_index,
end_day_index)
opt_handler = GPyOpt.methods.BayesianOptimization(
f=opt_func, # Objective function
domain=mixed_domain, # Box-constraints of the problem
initial_design_numdata=30, # Number data initial design
acquisition_type='EI', # Expected Improvement
exact_feval=True,
maximize=True) # True evaluations, no sample noise
opt_handler.run_optimization(max_iter, eps=0)
def opt_func(self, strategy_list, start_day, end_day, X_list):
assert (len(X_list) == 1)
features = X_list[0]
print("starting test: {}".format(self.get_parameter_str(features)))
cached_result, index = self.optimize_result.find_result(features)
if cached_result is not None:
total_profit = cached_result[0]
n_days = cached_result[1]
profit_mean = cached_result[2]
error_mean = cached_result[3]
print("find from cache. skip...")
else:
save_path = self.get_save_path(features)
stock_worm = StockWorm(self.stock_index, self.npy_files_path,
save_path)
total_profit, profit_daily, errors_daily = stock_worm.init(
features, strategy_list, start_day, end_day)
n_days = len(profit_daily)
profit_mean = np.mean(profit_daily)
error_mean = np.mean(errors_daily)
self.optimize_result.insert_result(
features, [total_profit, n_days, profit_mean, error_mean])
print("result saved to: {}".format(self.stock_worm_cache_file))
self.optimize_result.save(self.stock_worm_cache_file)
print(
"total_profit:{} in {} days, profit_mean:{} error:{} parameters:{}"
.format(total_profit, n_days, profit_mean, error_mean,
self.get_parameter_str(features)))
return np.array(profit_mean).reshape((1, 1))
def get_swarm_path(self, start_day_index, end_day_index):
return os.path.join(self.model_save_path,
"{}_{}".format(self.stock_name, self.stock_index),
"{}-{}".format(start_day_index, end_day_index))
def get_model_path(self, start_day_index, end_day_index):
swarm_path = self.get_swarm_path(start_day_index, end_day_index)
return os.path.join(swarm_path, "models")
def get_stockworm_cache_file(self, start_day_index, end_day_index):
swarm_path = self.get_swarm_path(start_day_index, end_day_index)
return os.path.join(swarm_path, "stockworm_cache.txt")
def get_strategy_cache_file(self, start_day_index, end_day_index):
swarm_path = self.get_swarm_path(start_day_index, end_day_index)
return os.path.join(swarm_path, "strategy_cache.txt")
def update_worms_from_cache(self, n_number, start_day_index,
end_day_index):
optimize_result = OptimizeResult(result_column_index=15)
stockworm_cache_file = self.get_stockworm_cache_file(
start_day_index, end_day_index)
optimize_result.load(stockworm_cache_file)
top_worms = optimize_result.get_best_results(n_number)
trade_strategy_factory = TradeStrategyFactory()
strategy_cache_file = self.get_strategy_cache_file(
start_day_index, end_day_index)
strategy_list = trade_strategy_factory.create_from_file(
strategy_cache_file, NUM_STRATEGIES)
assert (len(top_worms) == n_number)
swarm_path = self.get_swarm_path(start_day_index, end_day_index)
for i in range(n_number):
features = top_worms[i, :13]
features_str = self.get_parameter_str(features)
model_save_path = os.path.join(swarm_path, "models",
md5(features_str))
new_worm = StockWorm(self.stock_index, self.npy_files_path,
model_save_path)
if os.path.isdir(model_save_path) and new_worm.load() == True:
pass
else:
total_profit, profit_daily, errors_daily = new_worm.init(
features, strategy_list, start_day_index, end_day_index)
new_worm.save()
print("training finished for model {}, total_profit:{}".format(
i, total_profit))
testing_total_profit, testing_profit_daily, n_data_appended = new_worm.test(
)
if n_data_appended > 0:
print(
"testing finished for model {}, total_profit:{} in {} days, new data for {} days appended"
.format(i, testing_total_profit, len(testing_profit_daily),
n_data_appended))
new_worm.save()
self.worm_list.append(new_worm)
def report(self):
assert (self.worm_list is not None)
for i in range(len(self.worm_list)):
print("Report for Worm No.{}".format(i + 1))
self.worm_list[i].report()
def get_save_path(self, X):
params_str = self.get_parameter_str(X)
return os.path.join(self.model_save_path, md5(params_str))
def get_parameter_str(self, X):
parameter_str = ""
for i in range(len(self.mixed_domain)):
parameter_str += self.mixed_domain[i]["name"]
parameter_str += ':'
parameter_str += str(X[i])
parameter_str += ','
return parameter_str