def monitor(self, code):
data_set = stock_dataset()
data_set.load_from_file(code, "../data/" + code)
#把今天的数据加入到data_set里
fetcher = stock_fetcher()
net = fetcher.get_present_price(code).close_price
stock = stock_data()
stock.close_price = net
# today = datetime.now() - timedelta(days = 2) #周末测试时使用
today = datetime.now()
today = today.strftime("%Y-%m-%d")
stock.date = today
data_set.data.append(stock)
turtle_plan3 = turtle()
result = turtle_plan3.get_trading_plan3(data_set, today)
result["code"] = code
f = open("../result/turtle3_result", "r+")
line = f.readline()
f.close()
if line.startswith("#"):
result["share"] = 0
result["cost"] = 0
return result
arr = line.strip().split("|")
result["share"] = arr[5]
result["cost"] = arr[6]
# if(result["choise"] == 4): #buy
# result["share"] = round((money * (1 - DEAL_RATE) / result["close_price"]), 3)
# result["money"] = round(money * (1 - DEAL_RATE), 3)
# result["profit"] = profit
# self.append_result_to_file(filename, result, delim)
# elif(result["choise"] == 2): #sell
# result["share"] = share
# result["money"] = round(result["close_price"] * share * (1 - DEAL_RATE), 3)
# result["profit"] = result["money"] - money
# self.append_result_to_file(filename, result, delim)
return result
def test(dataset, dataset_name, start_money):
"""
turtle model test
"""
print "Dataset: %s, data_len=%d, start_date=%s, end_date=%s" % (dataset_name, len(dataset.data), dataset.data[0].date, dataset.data[-1].date)
# get close price
close_prices = [ item.close_price for item in dataset.data ]
# calculate other data
analyser = data_analyser()
mean_price_10 = analyser.calcu_mean_line(close_prices, 10)
mean_price_100 = analyser.calcu_mean_line(close_prices, 100)
max_price_50 = analyser.calcu_max_line(close_prices, 50)
min_price_50 = analyser.calcu_min_line(close_prices, 50)
min_price_25 = analyser.calcu_min_line(close_prices, 25)
# Initilize a lundong model, and get trading decisions by it
turtle_model = turtle()
model_results = []
for item in dataset.data:
model_results.append(turtle_model.get_trading_plan(dataset, item.date))
test_data = [ item["close_price"] for item in model_results ]
test_data = test_data[99:]
buy = []
buy_index = []
sell_half = []
sell_half_index = []
sell_all = []
sell_all_index = []
for i in range(len(model_results)):
if model_results[i]["choise"] == 4:
buy.append(model_results[i]["close_price"])
buy_index.append(i)
elif model_results[i]["choise"] == 0:
sell_all.append(model_results[i]["close_price"])
sell_all_index.append(i)
elif model_results[i]["choise"] == 1:
sell_half.append(model_results[i]["close_price"])
sell_half_index.append(i)
# trunk invalid data
close_prices = close_prices[99:]
mean_price_10 = mean_price_10[99:]
mean_price_100 = mean_price_100[99:]
max_price_50 = max_price_50[99:]
min_price_50 = min_price_50[99:]
min_price_25 = min_price_25[99:]
buy_index = [ num - 99 for num in buy_index]
sell_half_index = [ num - 99 for num in sell_half_index]
sell_all_index = [ num - 99 for num in sell_all_index]
plt.figure(1)
#plt.subplot(311)
#plt.plot(close_prices, 'b-', mean_price_10, 'g-', mean_price_100, 'r-', max_price_50, 'c-', min_price_50, 'm-', min_price_25, 'y-', test_data, 'go')
#plt.legend([dataset_name, "10 hour mean", "100 hour mean", "50 max price", '50 min price', '25 min price', 'test_data'], loc="upper left")
plt.plot(close_prices, 'b-', mean_price_10, 'g-', mean_price_100, 'r-', max_price_50, 'c-', min_price_50, 'm-', min_price_25, 'y-', buy_index, buy, 'ro', sell_all_index, sell_all, 'go', sell_half_index, sell_half, 'g^')
plt.legend([dataset_name, "10 hour mean", "100 hour mean", "50 max price", '50 min price', '25 min price', 'buy', 'sell_all', 'sell_half'], loc="upper left")
plt.grid(True)
plt.show()
return
def test(dataset, dataset_name, start_money):
"""
turtle model test
"""
print "Dataset: %s, data_len=%d, start_date=%s, end_date=%s" % (
dataset_name, len(
dataset.data), dataset.data[0].date, dataset.data[-1].date)
# get close price
close_prices = [item.close_price for item in dataset.data]
# calculate other data
analyser = data_analyser()
mean_price_10 = analyser.calcu_mean_line(close_prices, 10)
mean_price_100 = analyser.calcu_mean_line(close_prices, 100)
max_price_50 = analyser.calcu_max_line(close_prices, 50)
min_price_50 = analyser.calcu_min_line(close_prices, 50)
min_price_25 = analyser.calcu_min_line(close_prices, 25)
# Initilize a lundong model, and get trading decisions by it
turtle_model = turtle()
model_results = []
for item in dataset.data:
model_results.append(turtle_model.get_trading_plan(dataset, item.date))
test_data = [item["close_price"] for item in model_results]
test_data = test_data[99:]
buy = []
buy_index = []
sell_half = []
sell_half_index = []
sell_all = []
sell_all_index = []
for i in range(len(model_results)):
if model_results[i]["choise"] == 4:
buy.append(model_results[i]["close_price"])
buy_index.append(i)
elif model_results[i]["choise"] == 0:
sell_all.append(model_results[i]["close_price"])
sell_all_index.append(i)
elif model_results[i]["choise"] == 1:
sell_half.append(model_results[i]["close_price"])
sell_half_index.append(i)
# trunk invalid data
close_prices = close_prices[99:]
mean_price_10 = mean_price_10[99:]
mean_price_100 = mean_price_100[99:]
max_price_50 = max_price_50[99:]
min_price_50 = min_price_50[99:]
min_price_25 = min_price_25[99:]
buy_index = [num - 99 for num in buy_index]
sell_half_index = [num - 99 for num in sell_half_index]
sell_all_index = [num - 99 for num in sell_all_index]
plt.figure(1)
#plt.subplot(311)
#plt.plot(close_prices, 'b-', mean_price_10, 'g-', mean_price_100, 'r-', max_price_50, 'c-', min_price_50, 'm-', min_price_25, 'y-', test_data, 'go')
#plt.legend([dataset_name, "10 hour mean", "100 hour mean", "50 max price", '50 min price', '25 min price', 'test_data'], loc="upper left")
plt.plot(close_prices, 'b-', mean_price_10, 'g-', mean_price_100, 'r-',
max_price_50, 'c-', min_price_50, 'm-', min_price_25, 'y-',
buy_index, buy, 'ro', sell_all_index, sell_all, 'go',
sell_half_index, sell_half, 'g^')
plt.legend([
dataset_name, "10 hour mean", "100 hour mean", "50 max price",
'50 min price', '25 min price', 'buy', 'sell_all', 'sell_half'
],
loc="upper left")
plt.grid(True)
plt.show()
return
