def test(
data1,
data2,
data1_name,
data2_name,
data1_price,
data2_price,
data1_price_name,
data2_price_name,
start_money,
gap_days,
trading_day,
):
"""
lundong model test with etf
"""
print "Dataset1: %s, data_len=%d, start_date=%s, end_date=%s" % (
data1_name,
len(data1),
data1[0][DATE_POS],
data1[-1][DATE_POS],
)
print "Dataset2: %s, data_len=%d, start_date=%s, end_date=%s" % (
data2_name,
len(data2),
data2[0][DATE_POS],
data2[-1][DATE_POS],
)
print "Price1: %s, data_len=%d, start_date=%s, end_date=%s" % (
data1_price_name,
len(data1_price),
data1_price[0][DATE_POS],
data1_price[-1][DATE_POS],
)
print "Price2: %s, data_len=%d, start_date=%s, end_date=%s" % (
data2_price_name,
len(data2_price),
data2_price[0][DATE_POS],
data2_price[-1][DATE_POS],
)
# Preprocess datasets
print "\npreprocessing datasets...\n"
time_align(data1, data1_price)
time_align(data2, data2_price)
time_align(data1, data2)
time_align(data1, data1_price)
time_align(data2, data2_price)
time_truncate(data1, "2013-02-22", "2015-10-30")
time_truncate(data2, "2013-02-22", "2015-10-30")
time_truncate(data1_price, "2013-02-22", "2015-10-30")
time_truncate(data2_price, "2013-02-22", "2015-10-30")
print "Dataset1: %s, data_len=%d, start_date=%s, end_date=%s" % (
data1_name,
len(data1),
data1[0][DATE_POS],
data1[-1][DATE_POS],
)
print "Dataset2: %s, data_len=%d, start_date=%s, end_date=%s" % (
data2_name,
len(data2),
data2[0][DATE_POS],
data2[-1][DATE_POS],
)
print "Price1: %s, data_len=%d, start_date=%s, end_date=%s" % (
data1_price_name,
len(data1_price),
data1_price[0][DATE_POS],
data1_price[-1][DATE_POS],
)
print "Price2: %s, data_len=%d, start_date=%s, end_date=%s" % (
data2_price_name,
len(data2_price),
data2_price[0][DATE_POS],
data2_price[-1][DATE_POS],
)
# Get close price of two datasets and plot them
data1_close_price = [item[CLOSE_PRICE_POS] for item in data1]
data2_close_price = [item[CLOSE_PRICE_POS] for item in data2]
data1_price_close_price = [item[CLOSE_PRICE_POS] for item in data1_price]
data2_price_close_price = [item[CLOSE_PRICE_POS] for item in data2_price]
analyser = data_analyser()
analyser.draw_relevance(data1_close_price, data1_price_close_price, data1_name, data1_price_name)
analyser.draw_relevance(data2_close_price, data2_price_close_price, data2_name, data2_price_name)
plt.figure(1)
plt.subplot(311)
plt.plot(data1_close_price, "b-", data2_close_price, "r-")
plt.legend([data1_name, data2_name], loc="upper left")
plt.grid(True)
# Initilize a lundong model, and get trading decisions by it
lundong_model = lundong()
# the test start with a blank account
account = account_simulator(start_money)
account_real = account_simulator(start_money)
money = []
money_real = []
# record every trading plan
result = []
last_valid_trading_plan = {}
for item in data1:
trading_plan = lundong_model.get_trading_plan(
data1, data2, data1_name, data2_name, item[DATE_POS], gap_days, trading_day, last_valid_trading_plan
)
# if there is a trading plan
if trading_plan != {}:
# choise == 0 means buy data1 and sell data2
if trading_plan["choise"] == 0:
# fake
# if we have data2, sell it
stock = account.get_stock(data2_name)
if stock != None and stock.share != 0:
account.sell(data2_name, trading_plan["data2_close_price"], stock.share)
# buy data1 as many as we can
account.buy(
data1_name,
trading_plan["data1_close_price"],
int(math.floor(account.money / trading_plan["data1_close_price"])),
)
# real
# if we have data2, sell it
stock = account_real.get_stock(data2_price_name)
if stock != None and stock.share != 0:
real_data = get_data(data2_price, trading_plan["date_str"])
account_real.sell(data2_price_name, real_data[CLOSE_PRICE_POS], stock.share)
# buy data1 as many as we can
real_data = get_data(data1_price, trading_plan["date_str"])
account_real.buy(
data1_price_name,
real_data[CLOSE_PRICE_POS],
int(math.floor(account_real.money / real_data[CLOSE_PRICE_POS])),
)
# choise == 1 means sell data1 and buy data2
elif trading_plan["choise"] == 1:
# if we have data1, sell it
stock = account.get_stock(data1_name)
if stock != None and stock.share != 0:
account.sell(data1_name, trading_plan["data1_close_price"], stock.share)
# buy data2 as many as we can
account.buy(
data2_name,
trading_plan["data2_close_price"],
int(math.floor(account.money / trading_plan["data2_close_price"])),
)
# real
stock = account_real.get_stock(data1_price_name)
if stock != None and stock.share != 0:
real_data = get_data(data1_price, trading_plan["date_str"])
account_real.sell(data1_price_name, real_data[CLOSE_PRICE_POS], stock.share)
# buy data1 as many as we can
real_data = get_data(data2_price, trading_plan["date_str"])
account_real.buy(
data2_price_name,
real_data[CLOSE_PRICE_POS],
int(math.floor(account_real.money / real_data[CLOSE_PRICE_POS])),
)
# choise == 2 means sell all data2
elif trading_plan["choise"] == 2:
# if we have data1, sell it
stock = account.get_stock(data1_name)
if stock != None and stock.share != 0:
account.sell(data1_name, trading_plan["data1_close_price"], stock.share)
# if we have data2, sell it
stock = account.get_stock(data2_name)
if stock != None and stock.share != 0:
account.sell(data2_name, trading_plan["data2_close_price"], stock.share)
stock = account_real.get_stock(data1_price_name)
if stock != None and stock.share != 0:
real_data = get_data(data1_price, trading_plan["date_str"])
account_real.sell(data1_price_name, real_data[CLOSE_PRICE_POS], stock.share)
stock = account_real.get_stock(data2_price_name)
if stock != None and stock.share != 0:
real_data = get_data(data2_price, trading_plan["date_str"])
account_real.sell(data2_price_name, real_data[CLOSE_PRICE_POS], stock.share)
# other choises means do not trade
else:
pass
# if there is no trading plan, do not trade
else:
pass
money.append(account.get_value())
money_real.append(account_real.get_value())
# money.append(account.money)
result.append(trading_plan)
if trading_plan != {} and trading_plan["choise"] != -1:
last_valid_trading_plan = trading_plan
# Get rising rates, choises and plot them
data1_up = []
data2_up = []
choise_0 = []
choise_0_index = []
choise_1 = []
choise_1_index = []
choise_2 = []
choise_2_index = []
i = 0
for item in result:
if item == {}:
data1_up.append(0.0)
data2_up.append(0.0)
else:
data1_up.append(item["data1_up"])
data2_up.append(item["data2_up"])
if item["choise"] == 0:
choise_0.append(item["data1_up"])
choise_0_index.append(i)
elif item["choise"] == 1:
choise_1.append(item["data2_up"])
choise_1_index.append(i)
elif item["choise"] == 2:
choise_2.append(0)
choise_2_index.append(i)
i = i + 1
plt.subplot(312)
plt.plot(
range(len(data1_up)),
data1_up,
"b-",
range(len(data2_up)),
data2_up,
"r-",
choise_0_index,
choise_0,
"bo",
choise_1_index,
choise_1,
"ro",
choise_2_index,
choise_2,
"r^",
)
# plt.plot(data1_up, 'b-', data2_up, 'r-', choise_0, 'bo', choise_1, 'r^')
plt.legend(
[data1_name, data2_name, "switch to:" + data1_name, "switch to:" + data2_name, "sell all"], loc="upper left"
)
plt.grid(True)
plt.subplot(313)
plt.plot(
[item / start_money for item in money],
"g-",
[item / start_money for item in money_real],
"k-",
[float(item[CLOSE_PRICE_POS]) / float(data1_price[0][CLOSE_PRICE_POS]) for item in data1_price],
"b-",
[float(item[CLOSE_PRICE_POS]) / float(data2_price[0][CLOSE_PRICE_POS]) for item in data2_price],
"r-",
)
plt.legend(["model", "real", data1_price_name, data2_price_name], loc="upper left")
plt.grid(True)
# plt.show()
account.dump()
return account.get_value() / start_money, account_real.get_value() / start_money
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(data1, data2, data1_name, data2_name, data1_price, data2_price, data1_price_name, data2_price_name, start_money, gap_days, trading_day):
"""
lundong model test with etf
"""
print "Dataset1: %s, data_len=%d, start_date=%s, end_date=%s" % (data1_name, len(data1), data1[0][DATE_POS], data1[-1][DATE_POS])
print "Dataset2: %s, data_len=%d, start_date=%s, end_date=%s" % (data2_name, len(data2), data2[0][DATE_POS], data2[-1][DATE_POS])
print "Price1: %s, data_len=%d, start_date=%s, end_date=%s" % (data1_price_name, len(data1_price), data1_price[0][DATE_POS], data1_price[-1][DATE_POS])
print "Price2: %s, data_len=%d, start_date=%s, end_date=%s" % (data2_price_name, len(data2_price), data2_price[0][DATE_POS], data2_price[-1][DATE_POS])
# Preprocess datasets
print "\npreprocessing datasets...\n"
time_align(data1, data1_price)
time_align(data2, data2_price)
time_align(data1, data2)
time_align(data1, data1_price)
time_align(data2, data2_price)
time_truncate(data1, "2013-02-22", "2015-10-30")
time_truncate(data2, "2013-02-22", "2015-10-30")
time_truncate(data1_price, "2013-02-22", "2015-10-30")
time_truncate(data2_price, "2013-02-22", "2015-10-30")
print "Dataset1: %s, data_len=%d, start_date=%s, end_date=%s" % (data1_name, len(data1), data1[0][DATE_POS], data1[-1][DATE_POS])
print "Dataset2: %s, data_len=%d, start_date=%s, end_date=%s" % (data2_name, len(data2), data2[0][DATE_POS], data2[-1][DATE_POS])
print "Price1: %s, data_len=%d, start_date=%s, end_date=%s" % (data1_price_name, len(data1_price), data1_price[0][DATE_POS], data1_price[-1][DATE_POS])
print "Price2: %s, data_len=%d, start_date=%s, end_date=%s" % (data2_price_name, len(data2_price), data2_price[0][DATE_POS], data2_price[-1][DATE_POS])
# Get close price of two datasets and plot them
data1_close_price = [ item[CLOSE_PRICE_POS] for item in data1 ]
data2_close_price = [ item[CLOSE_PRICE_POS] for item in data2 ]
data1_price_close_price = [ item[CLOSE_PRICE_POS] for item in data1_price ]
data2_price_close_price = [ item[CLOSE_PRICE_POS] for item in data2_price ]
analyser = data_analyser()
analyser.draw_relevance(data1_close_price, data1_price_close_price, data1_name, data1_price_name)
analyser.draw_relevance(data2_close_price, data2_price_close_price, data2_name, data2_price_name)
plt.figure(1)
plt.subplot(311)
plt.plot(data1_close_price, 'b-', data2_close_price, 'r-')
plt.legend([data1_name, data2_name], loc="upper left")
plt.grid(True)
# Initilize a lundong model, and get trading decisions by it
lundong_model = lundong()
# the test start with a blank account
account = account_simulator(start_money)
account_real = account_simulator(start_money)
money = []
money_real = []
# record every trading plan
result = []
last_valid_trading_plan = {}
for item in data1:
trading_plan = lundong_model.get_trading_plan(data1, data2, data1_name, data2_name, item[DATE_POS], gap_days, trading_day, last_valid_trading_plan)
# if there is a trading plan
if trading_plan != {}:
# choise == 0 means buy data1 and sell data2
if trading_plan["choise"] == 0:
# fake
# if we have data2, sell it
stock = account.get_stock(data2_name)
if stock != None and stock.share != 0:
account.sell(data2_name, trading_plan["data2_close_price"], stock.share)
# buy data1 as many as we can
account.buy(data1_name, trading_plan["data1_close_price"], int(math.floor(account.money / trading_plan["data1_close_price"])))
# real
# if we have data2, sell it
stock = account_real.get_stock(data2_price_name)
if stock != None and stock.share != 0:
real_data = get_data(data2_price, trading_plan["date_str"])
account_real.sell(data2_price_name, real_data[CLOSE_PRICE_POS], stock.share)
# buy data1 as many as we can
real_data = get_data(data1_price, trading_plan["date_str"])
account_real.buy(data1_price_name, real_data[CLOSE_PRICE_POS], int(math.floor(account_real.money / real_data[CLOSE_PRICE_POS])))
# choise == 1 means sell data1 and buy data2
elif trading_plan["choise"] == 1:
# if we have data1, sell it
stock = account.get_stock(data1_name)
if stock != None and stock.share != 0:
account.sell(data1_name, trading_plan["data1_close_price"], stock.share)
# buy data2 as many as we can
account.buy(data2_name, trading_plan["data2_close_price"], int(math.floor(account.money / trading_plan["data2_close_price"])))
# real
stock = account_real.get_stock(data1_price_name)
if stock != None and stock.share != 0:
real_data = get_data(data1_price, trading_plan["date_str"])
account_real.sell(data1_price_name, real_data[CLOSE_PRICE_POS], stock.share)
# buy data1 as many as we can
real_data = get_data(data2_price, trading_plan["date_str"])
account_real.buy(data2_price_name, real_data[CLOSE_PRICE_POS], int(math.floor(account_real.money / real_data[CLOSE_PRICE_POS])))
# choise == 2 means sell all data2
elif trading_plan["choise"] == 2:
# if we have data1, sell it
stock = account.get_stock(data1_name)
if stock != None and stock.share != 0:
account.sell(data1_name, trading_plan["data1_close_price"], stock.share)
# if we have data2, sell it
stock = account.get_stock(data2_name)
if stock != None and stock.share != 0:
account.sell(data2_name, trading_plan["data2_close_price"], stock.share)
stock = account_real.get_stock(data1_price_name)
if stock != None and stock.share != 0:
real_data = get_data(data1_price, trading_plan["date_str"])
account_real.sell(data1_price_name, real_data[CLOSE_PRICE_POS], stock.share)
stock = account_real.get_stock(data2_price_name)
if stock != None and stock.share != 0:
real_data = get_data(data2_price, trading_plan["date_str"])
account_real.sell(data2_price_name, real_data[CLOSE_PRICE_POS], stock.share)
# other choises means do not trade
else:
pass
# if there is no trading plan, do not trade
else:
pass
money.append(account.get_value())
money_real.append(account_real.get_value())
#money.append(account.money)
result.append(trading_plan)
if trading_plan != {} and trading_plan["choise"] != -1:
last_valid_trading_plan = trading_plan
# Get rising rates, choises and plot them
data1_up = []
data2_up = []
choise_0 = []
choise_0_index = []
choise_1 = []
choise_1_index = []
choise_2 = []
choise_2_index = []
i = 0
for item in result:
if item == {}:
data1_up.append(0.0)
data2_up.append(0.0)
else:
data1_up.append(item["data1_up"])
data2_up.append(item["data2_up"])
if item["choise"] == 0:
choise_0.append(item["data1_up"])
choise_0_index.append(i)
elif item["choise"] == 1:
choise_1.append(item["data2_up"])
choise_1_index.append(i)
elif item["choise"] == 2:
choise_2.append(0)
choise_2_index.append(i)
i = i + 1
plt.subplot(312)
plt.plot(range(len(data1_up)), data1_up, 'b-', range(len(data2_up)), data2_up, 'r-',\
choise_0_index, choise_0, 'bo', choise_1_index, choise_1, 'ro',\
choise_2_index, choise_2, 'r^')
#plt.plot(data1_up, 'b-', data2_up, 'r-', choise_0, 'bo', choise_1, 'r^')
plt.legend([data1_name, data2_name, "switch to:" + data1_name, "switch to:" + data2_name, "sell all"], loc="upper left")
plt.grid(True)
plt.subplot(313)
plt.plot([item / start_money for item in money], 'g-', \
[item / start_money for item in money_real], 'k-', \
[ float(item[CLOSE_PRICE_POS]) / float(data1_price[0][CLOSE_PRICE_POS]) for item in data1_price], 'b-', \
[ float(item[CLOSE_PRICE_POS]) / float(data2_price[0][CLOSE_PRICE_POS]) for item in data2_price], 'r-')
plt.legend(["model", "real", data1_price_name, data2_price_name], loc="upper left")
plt.grid(True)
#plt.show()
account.dump()
return account.get_value() / start_money, account_real.get_value() / start_money
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
