class PeriodSimulate(Simulate):
def __init__(self, stock_codes, judge_out_name, period=7):
super().__init__(stock_codes, judge_out_name)
self.period = period
@staticmethod
def get_sum_field():
return ['win_pct_less_than_50', 'win_pct_bigger_than_50', 'win_pct_bigger_than_60', 'win_pct_bigger_than_70']
def __call__(self, *args, **kwargs):
self.data_center = DataCenter()
if self.stock_codes is None or len(self.stock_codes) == 0:
return
# 统计所有的股票，在:param peirod期间之内的盈利情况
sum_dict = {
'win_pct_less_than_50': 0,
'win_pct_bigger_than_50': 0,
'win_pct_bigger_than_60': 0,
'win_pct_bigger_than_70': 0,
}
for stock_code in self.stock_codes:
base_infos = self.data_center.fetch_base_data(stock_code)
shift_close = base_infos['close'].shift(self.period)
win_pct = (shift_close - base_infos['close']) / base_infos['close']
base_infos['win_pct'] = win_pct
base_infos.drop(['vol', 'amount', 'pre_close', 'change'], axis=1, inplace=True)
if not base_infos.empty:
count_percent = len(base_infos[base_infos['win_pct'] > 0]) / len(base_infos)
sum_dict['win_pct_less_than_50'] = sum_dict['win_pct_less_than_50'] + 1 if count_percent < 0.5 \
else sum_dict['win_pct_less_than_50']
sum_dict['win_pct_bigger_than_50'] = sum_dict['win_pct_bigger_than_50'] + 1 if count_percent >= 0.5 \
else sum_dict['win_pct_bigger_than_50']
sum_dict['win_pct_bigger_than_60'] = sum_dict['win_pct_bigger_than_60'] + 1 if count_percent >= 0.6 \
else sum_dict['win_pct_bigger_than_60']
sum_dict['win_pct_bigger_than_70'] = sum_dict['win_pct_bigger_than_70'] + 1 if count_percent >= 0.7 \
else sum_dict['win_pct_bigger_than_70']
sum_str = "win count percent is " + str(count_percent)
file_name = "period_simulate_" + stock_code
now_time = datetime.datetime.now()
now_time_str = now_time.strftime('%Y%m%d')
file_name += '_' + now_time_str
file_name += '.csv'
FileOutput.csv_output(None, base_infos, file_name, spe_dir_name=self.judge_out_name,
extra_content=sum_str)
return sum_dict
class Simulate:
BUY_FLAG = 1
SOLD_FLAG = -1
DO_NOTHING = 0
MULTI_INDI_BETWEEN = 5 # 如果有多种指标，多少天之内均发出买入信号就决定买入
def __init__(self, stock_codes, judge_out_name, judge_time=None):
self.initial_mny = None
self.stock_codes = stock_codes
self.judge_time = judge_time
self.hold_num = 0 # 持仓手数
self.left_mny = 0 # 剩余金额
self.judge_out_name = judge_out_name # 判定输出目录名称
def set_registry(self, judge_time):
self.judge_time = judge_time
def set_judge_name(self, judge_name):
self.judge_out_name = judge_name
def set_init_mny(self, init_mny):
"""
设置初始金额，金额为RMB
:param init_mny:
:return:
"""
self.initial_mny = init_mny
self.left_mny = init_mny
def reset(self):
self.left_mny = self.initial_mny
self.hold_num = 0
def __call__(self, *args, **kwargs):
# TODO -- 看一下Python的数值计算方式，是否有小数？？？
self.data_center = DataCenter()
final_sum_dict = {
'win_num': 0,
'lose_num': 0,
'max_win_pct': 0,
'max_lost_pct': 0,
'ave_win_mny': 0
}
trade_rst = pandas.DataFrame(columns=('ts_code', 'trade_times', 'final_win', 'win_pct'))
for stock_code in self.stock_codes:
trade_rst_dict = {
'ts_code': stock_code,
'trade_times': 0,
'final_win': 0,
'win_pct': 0
}
base_infos = self.data_center.fetch_base_data(stock_code)
if base_infos is None or len(base_infos) == 0:
continue
ret_time = []
for item in self.judge_time:
ret_time.append(item(base_infos))
if ret_time is None or len(ret_time) == 0:
continue
detail_trade_info = pandas.DataFrame(
columns=('ts_code', 'curr_close', 'trade_date', 'trade_num', 'hold_num', 'hold_mny',
'total_mny'))
if len(ret_time[0]) != len(base_infos):
print("Not equals!!!")
for i in range(len(ret_time[0])):
# 判定是否是买入时机
operate_flag = self.DO_NOTHING
temp_buy_pct = 0.1
for item in ret_time:
# 从当前往前5天之内是否有发出过买入信号，如果有，就算有
start_index = i - self.MULTI_INDI_BETWEEN
start_index = 0 if start_index < 0 else start_index
temp_val = self.DO_NOTHING
for j in range(start_index, i + 1):
if item.at[j, 'flag'] == self.BUY_FLAG:
temp_val = self.BUY_FLAG
temp_buy_pct = item.at[j, 'percent'] if 0 < item.at[j, 'percent'] < temp_buy_pct \
else temp_buy_pct
break
elif item.at[j, 'flag'] == self.SOLD_FLAG:
temp_val = self.SOLD_FLAG
temp_buy_pct = item.at[j, 'percent'] if 0 < item.at[j, 'percent'] < temp_buy_pct \
else temp_buy_pct
break
if operate_flag == self.DO_NOTHING or operate_flag == temp_val:
operate_flag = temp_val
continue
else:
operate_flag = self.DO_NOTHING
break
if operate_flag == self.BUY_FLAG:
# 默认买入10%
buy_pct = temp_buy_pct
buy_mny = self.initial_mny * buy_pct
buy_mny = self.left_mny if self.left_mny < buy_mny else buy_mny
buy_num = buy_mny / base_infos.at[i, 'close']
self.hold_num = self.hold_num + buy_num
self.left_mny = self.left_mny - buy_mny
hold_mny = self.hold_num * base_infos.at[i, 'close']
# 记录买入信息
temp_dict = {
'ts_code': stock_code,
'trade_date': base_infos.at[i, 'trade_date'],
'trade_num': buy_num,
'hold_num': self.hold_num,
'hold_mny': hold_mny,
'total_mny': self.left_mny + hold_mny,
'curr_close': base_infos.at[i, 'close']
}
detail_trade_info = detail_trade_info.append(temp_dict, ignore_index=True)
trade_rst_dict['trade_times'] = trade_rst_dict['trade_times'] + 1
elif operate_flag == self.SOLD_FLAG:
# 默认卖出持仓数量的10%
sold_pct = temp_buy_pct
sold_num = self.hold_num * sold_pct
sold_num = sold_num / 100 * 100
self.hold_num = self.hold_num - sold_num
sold_mny = sold_num * base_infos.at[i, 'close']
self.left_mny = self.left_mny + sold_mny
hold_mny = self.hold_num * base_infos.at[i, 'close']
# 记录卖出信息
temp_dict = {
'ts_code': stock_code,
'trade_date': base_infos.at[i, 'trade_date'],
'trade_num': sold_num,
'hold_num': self.hold_num,
'hold_mny': hold_mny,
'total_mny': self.left_mny + hold_mny,
'curr_close': base_infos.at[i, 'close']
}
detail_trade_info = detail_trade_info.append(temp_dict, ignore_index=True)
trade_rst_dict['trade_times'] = trade_rst_dict['trade_times'] + 1
if not detail_trade_info.empty:
file_name = "simulate_" + str(stock_code)
now_time = datetime.datetime.now()
now_time_str = now_time.strftime('%Y%m%d')
file_name += '_' + now_time_str
file_name += '.csv'
FileOutput.csv_output(None, detail_trade_info, file_name, spe_dir_name=self.judge_out_name)
else:
print("no such stock!")
self.reset()
if detail_trade_info.empty:
continue
# 统计该只股票的最终盈利
last_win = detail_trade_info.at[(len(detail_trade_info) - 1), 'total_mny'] - self.initial_mny
last_win_pct = last_win / self.initial_mny
trade_rst_dict['final_win'] = last_win
trade_rst_dict['win_pct'] = last_win_pct
trade_rst = trade_rst.append(trade_rst_dict, ignore_index=True)
# 统计多只股票的汇总
final_sum_dict['win_num'] = final_sum_dict['win_num'] + 1 if last_win > 0 else final_sum_dict['win_num']
final_sum_dict['lose_num'] = final_sum_dict['lose_num'] + 1 if last_win <= 0 else final_sum_dict['lose_num']
final_sum_dict['max_win_pct'] = last_win_pct if last_win_pct > final_sum_dict['max_win_pct'] else \
final_sum_dict['max_win_pct']
final_sum_dict['max_lost_pct'] = last_win_pct if last_win_pct <= final_sum_dict['max_lost_pct'] else \
final_sum_dict['max_lost_pct']
sum_str = "获利数量：" + str(final_sum_dict['win_num']) + " 损失数量：" + str(final_sum_dict['lose_num']) \
+ " 最大获利百分比：" + str(final_sum_dict['max_win_pct']) + " 最大损失百分比：" + \
str(final_sum_dict['max_lost_pct'])
if not trade_rst.empty:
file_name = "trade_group_" + self.stock_codes[0]
now_time = datetime.datetime.now()
now_time_str = now_time.strftime('%Y%m%d')
file_name += '_' + now_time_str
file_name += '.csv'
FileOutput.csv_output(None, trade_rst, file_name, spe_dir_name=self.judge_out_name, extra_content=sum_str)
return final_sum_dict
class MACDSelector:
"""
通过MACD指标来选择对应的股票
"""
BUY_SIGNAL_PERIOD = 2 # 两天之内发出买入信号的话，该只股票视为可买入
def __init__(self, ts_codes=None):
if ts_codes is None:
ts_codes = []
self.ts_codes = ts_codes
self.fast = 12
self.slow = 26
self.signal = 9
def set_fast(self, fast):
self.fast = fast
def set_slow(self, slow):
self.slow = slow
def set_signal(self, signal):
self.signal = signal
def __call__(self, *args, **kwargs):
self.data_center = DataCenter()
if len(self.ts_codes) == 0:
return
result = pandas.DataFrame(columns=('ts_code', 'in_price', 'in_date',
'origin_from', 'in_reason',
'finished', 'manual'))
for ts_code in self.ts_codes:
field_suffix = "_" + str(self.fast) + "_" + str(
self.slow) + "_" + str(self.signal)
macd_field_name = 'MACD' + field_suffix
histogram_field_name = 'MACDh' + field_suffix
signal_field_name = 'MACDs' + field_suffix
base_infos = self.data_center.fetch_base_data(ts_code)
if base_infos is None or len(base_infos) == 0:
continue
close = base_infos['close']
macd_ret = ta.macd(close, self.fast, self.slow, signal=self.signal)
if len(macd_ret) < 2:
continue
rst_length = len(macd_ret)
start_index = min(rst_length, self.BUY_SIGNAL_PERIOD)
start_index = rst_length - start_index
low_flag = False
for i in range(start_index, rst_length):
if macd_ret.at[i, macd_field_name] is not None and macd_ret.at[i, signal_field_name] is not None and \
macd_ret.at[i, macd_field_name] < macd_ret.at[i, signal_field_name]:
low_flag = True
else:
if low_flag:
now_time = datetime.datetime.now()
now_time_str = now_time.strftime('%Y%m%d')
temp_dict = {
'ts_code': ts_code,
'in_price': close[i],
'in_date': base_infos.at[start_index,
'trade_date'],
'origin_from': 'macd',
'in_reason': 'macd金叉',
'finished': 0,
'manual': 0
}
result = result.append(temp_dict, ignore_index=True)
return result