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 = []
for i in range(n_number):
X_list = data[i, :4]
trade_strategy_list.append(
TradeStrategy(X_list, self.n_max_trades_per_day, self.slippage,
self.courtage))
return trade_strategy_list
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 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)
stock_name = sys.argv[1]
stock_id = get_stock_id_by_name(stock_name)
training_start_day_index = int(sys.argv[2])
training_end_day_index = int(sys.argv[3])
number = 10
if len(sys.argv) == 5:
number = int(sys.argv[4])
swarm_dir = get_swarm_dir(stock_name, stock_id, training_start_day_index,
training_end_day_index)
strategy_file = os.path.join(swarm_dir, 'stockworm_cache.txt')
worm_results = OptimizeResult(-1)
worm_results.load(strategy_file)
print("Top 10 Worms in {} results for {}: swarm: {}-{}".format(
worm_results.get_size(), stock_name, training_start_day_index,
training_end_day_index))
worm_results.get_best_results(number)
columns = [
'n_neurons', 'learning_rate', 'num_layers', 'rnn_type', 'learning_period',
'prediction_period', 'n_repeats', 'beta', 'ema', 'time_format',
'volume_input', 'use_centralized_bid', 'split_daily_data', 'is_stateful',
'ref_stock_id', 'buy_threshold', 'sell_threshold', 'stop_gain',
'stop_gain', 'skip_at_beginning', 'value_ma', 'total_profit', 'days',
'avg_error', 'avg_profit'
]
print("Columns:")
for i in range(len(columns)):
print("{}: {}".format(i, columns[i]))
if len(sys.argv) < 5:
print(
"usage: python3 search-worms.py stock_name, stock_index traning_start_day_index, training_end_day_index"
)
sys.exit()
stock_name = sys.argv[1]
stock_index = int(sys.argv[2])
training_start_day_index = int(sys.argv[3])
training_end_day_index = int(sys.argv[4])
swarm_dir = get_swarm_dir(stock_name, stock_index, training_start_day_index,
training_end_day_index)
strategy_file = os.path.join(swarm_dir, 'strategy_cache.txt')
result_strategies = OptimizeResult(-1)
result_strategies.load(strategy_file)
print("Top 10 Strategies in {} results for {}: swarm: {}-{}".format(
result_strategies.get_size(), stock_name, training_start_day_index,
training_end_day_index))
result_strategies.get_best_results(10)
strategy_file = os.path.join(swarm_dir, 'stockworm_cache.txt')
worm_results = OptimizeResult(-2)
worm_results.load(strategy_file)
print("Top 10 Worms in {} results for {}: swarm: {}-{}".format(
worm_results.get_size(), stock_name, training_start_day_index,
training_end_day_index))
worm_results.get_best_results(10)
)
sys.exit()
stock_name = sys.argv[1]
start_day_index = int(sys.argv[2])
end_day_index = int(sys.argv[3])
if len(sys.argv) == 5:
number = int(sys.argv[4])
else:
number = 1
cache_file = get_cache_filename(stock_name, start_day_index, end_day_index)
optimize_result = OptimizeResult()
optimize_result.load(cache_file)
top_worms = optimize_result.get_best_results(number)
for i in range(len(top_worms)):
features = top_worms[i, :6]
strategy_features = top_worms[i, 6:12]
md5 = top_worms[i, -1]
model_path = get_model_path(stock_name, start_day_index, end_day_index)
save_path = os.path.join(model_path, md5)
hmm_model_filename = os.path.join(save_path, "hmm_model.pkl")
hmm_model = None
if os.path.isfile(hmm_model_filename):
print("file: {} exists, load it.".format(hmm_model_filename))
with open(hmm_model_filename, "rb") as file:
hmm_model = pickle.load(file)
else:
print("file: {} does not exists, generate it.".format(