def test_main(symbols, start_date, end_date, opt_month, val_month, test_month):
fig_title = fig_title_generate(symbols) # 用于生成图表的标题
order_list = [] # 存放回测结果
parameters = pd.DataFrame()
# 数据读取
file_dir = "Data//TestData//"
result_dir = "Data//Backresult//"
file_paths = [file_dir + forex + '1.csv' for forex in symbols]
data = read_mt4_data(file_paths, start=start_date, end=end_date)
total_month = num_month_get(start_date, end_date) # 总回测月数
iteration = num_iteration_get(total_month, opt_month, val_month,
test_month)
evaluation_factor = 0
for i in range(iteration):
_start = date_handle(start_date, i * opt_month)
_end = date_handle(_start, opt_month)
opt_period = test_period_generate(_start, _end)
my_data = Data()
my_data.data = [dt.ix[_start:_end] for dt in data]
# pop 存放优化后的策略参数
pop = optimize(data=my_data)
pop = np.array(pop) # 把pop类型由individual转化为array类型，方便处理
a = np.array(pd.DataFrame(pop).drop_duplicates()) # 去除pop里边重复的参数
# 参数验证
num_avg = len(a) # 参数个数
val = Validation.StrategyValidation(symbols) # 生成参数验证实例
if val_month == 0:
pass
else:
_start = _end # 验证开始时间
_end = date_handle(_start, val_month) # 验证结束时间
val.data = [dt.ix[_start:_end] for dt in data] # 验证数据
val_dict = {} # 保存策略参数，类型为字典
# 参数遍历,返回优化后的参数
for j in xrange(num_avg):
_avg = a[j]
immediate = val.capital(_avg)
val_dict[immediate] = _avg
immediate_keys = val_dict.keys()
immediate_keys.sort()
avg = val_dict[immediate_keys[-1]]
# 保存最优参数
opt_path = result_dir + 'opt_result' + "//" + opt_period + "//"
if os.path.exists(opt_path):
pass
else:
os.makedirs(opt_path)
opt_result_save(symbols, avg, my_data.data, opt_path)
parameter = paramater_save(symbols, opt_period, avg, opt_month)
parameters = parameters.append(copy.deepcopy(parameter))
_start = _end # 实测开始时间
_end = date_handle(_start, test_month) # 实测结束时间
if end_date_compare(end_date, _end):
_end = end_date
test_data = [dt.ix[_start:_end] for dt in data]
val.data = test_data
alt_parameter, alt_factor = val.parameter_exchange()
if (evaluation_factor < 0 and alt_factor > 0):
avg = alt_parameter
bt = TradeTest(parameter_match(symbols, avg))
bt.init_data = test_data
temp = bt.on_test()
evaluation_factor = strategy_evaluate(temp)
test_period = test_period_generate(_start, _end)
test_path = result_dir + 'test_result' + "//" + test_period + "//"
if os.path.exists(test_path):
pass
else:
os.makedirs(test_path)
backresult = cal_backtest_result(temp,
fig_title,
plot_date=False,
dir_path=test_path)
account_plot(bt.account, bt.init_data, test_path, plot_show=False)
save_backresult(backresult, test_path)
if temp:
order_list.extend(temp)
parameters.to_csv("Data//Backresult//parameters.csv")
# 计算回测结果
backresult = cal_backtest_result(order_list, fig_title, plot_date=False)
save_backresult(backresult)
