def generate_signal(context, code_list, stocks_info, lock, round_):
"""
生成策略信号
:param round_:
:param lock:
:param context:
:param code_list:
:param stocks_info:
:return:
"""
short_code_list = code_list.apply(
dataUtil.get_short_code) # 把 sh.600000 替换成 sh600000
# 获取当前价格
current_data_df = dataUtil.get_current_data(short_code_list, lock)
# 遍历每个小组内的code
for code in zip(code_list):
code = code[0]
# 获取历史价格
history_data = baoStockUtil.attribute_history(context, code, 90)
try:
# 填充基本信息
current_data = dataUtil.update_base_info(stocks_info,
current_data_df, code)
except KeyError:
logger.error("未查询到" + code + "今日的实时价格")
continue
# 执行策略
w_shape.strategy_w_shape(code, current_data, history_data, round_)
def generate_signal(context, all_code_list, stocks_info):
"""
生成每只股票的策略信号
"""
# 将全部code按每组chunk_len个进行分组
chunk_len = 50
for code_list in np.array_split(all_code_list,
len(all_code_list) / chunk_len + 1):
short_code_list = code_list.apply(
baoStockUtil.get_short_code) # 把 sh.600000 替换成 sh600000
# 获取当前价格
current_data_df = baoStockUtil.get_current_data(short_code_list)
# 遍历每个小组内的code
for code in zip(code_list):
code = code[0]
# 获取历史价格
history_data = baoStockUtil.attribute_history(context, code, 30)
try:
# 更新基本信息
current_data = baoStockUtil.update_base_info(
stocks_info, current_data_df, code)
except KeyError:
logger.error("未查询到" + code + "今日的实时价格")
continue
# 执行策略
doubleLine.strategy_ma30(code, stocks_info, current_data,
history_data)
doubleLine.strategy_ma8(code, stocks_info, current_data,
history_data)
def run():
plt_df = pd.DataFrame(index=pd.to_datetime(
context.date_range).strftime('%Y-%m-%d'),
columns=['mean'])
# 均线时间（天）
g.date = 20
# 布林带宽度
g.k = 1.5
for cursor_date in context.date_range:
# 计算游标日期
context.cursor_date = dateutil.parser.parse(cursor_date)
sr = baoStockUtil.attribute_history(context, g.security,
g.date)['close']
# 均线
ma = sr.mean()
# 上线
up = ma + g.k * sr.std()
# 下线
down = ma - g.k * sr.std()
# 当前价格
price = baoStockUtil.get_today_data(context, g.security)['open']
plt_df.loc[cursor_date, 'mean'] = ma
plt_df.loc[cursor_date, 'price'] = price
plt_df.loc[cursor_date, 'up'] = up
plt_df.loc[cursor_date, 'down'] = down
# 画图
plt_df[['price', 'up', 'down']].plot()
plt.show()
def handle_data(context):
# 双均线策略
hist = baoStockUtil.attribute_history(context, g.security, g.p2)
ma5 = hist['close'][-g.p1:].mean()
ma60 = hist['close'].mean()
if ma5 > ma60 and g.security not in context.positions:
orderUtil.order_value(context, g.security, context.cash)
elif ma5 < ma60 and g.security in context.positions:
orderUtil.order_target(context, g.security, 0)
def handle_data(context, data=None):
sr = baoStockUtil.attribute_history(context, g.security, g.M)['close']
# 均线
ma = sr.mean()
# 上线
up = ma + g.k * sr.std()
# 下线
down = ma - g.k * sr.std()
# 当前价格
p = baoStockUtil.get_today_data(context, g.security)['open']
# 可用金额
cash = context.cash
cursor_date = context.cursor_date.strftime("%Y-%m-%d")
if p < down and g.security not in context.positions:
print("%s价格%s低于支撑线，进行买入" % (cursor_date, p))
orderUtil.order_value(context, g.security, cash)
elif p > up and g.security in context.positions:
print("%s价格%s高于压力线，进行卖出" % (cursor_date, p))
orderUtil.order_target(context, g.security, 0)
def run():
min_float_per = 20 # 触底条件，大于最小值的百分之多少
rise_per_condition = 8 # 涨幅条件
# sample_stocks = historyUtil.get_sample_stocks('all')['code']
sample_stocks = baoStockUtil.get_sample_stocks('sz50')['code']
# sample_stocks = historyUtil.get_sample_stocks('zz500')['code']
for value in zip(sample_stocks):
security = value[0]
# print("\n开始解析"+security)
plt_df = baoStockUtil.attribute_history(context, security, 180)
min_price = plt_df['close'].min()
mean_price = plt_df['close'].mean()
max_price = plt_df['close'].max()
yesterday_open = plt_df['open'][-1]
yesterday_close = plt_df['close'][-1]
rise_flag = False
min_flag = False
#涨跌幅
pctChg = plt_df['pctChg'][-1]
if pctChg >= rise_per_condition:
rise_flag = True
min_per = (yesterday_open - min_price) / min_price * 100 # 昨日价格与最低价的比值
if min_per < min_float_per:
min_flag = True
if rise_flag & min_flag:
print('%s符合条件' % security)
print("从%s开始，最小值：%f，最大值：%f，平均值：%f" % (START_DATE,min_price, max_price, mean_price))
print("昨日开盘：%f，昨日收盘：%f，涨幅：%s" % (yesterday_open, yesterday_close, mainUtil.parse_percent(pctChg)))
print("分析完成")