def get_average_bond(data):
# 参数 偏移百分比 均线价格与当前价格的偏差 单位 百分
float_per = 1
items = data["data"]["item"]
current_data = items[-1]
#计算5日均线
five_price = calculate_util.calculate_average(items, 5)
# 计算5日价格与当前价格的相差百分比
five_diff_rate = calculate_util.calculate_rate(current_data[5], five_price)
# 判断 5日均线与当前价格差率是否在范围内
if (five_diff_rate > float_per):
return
ten_price = calculate_util.calculate_average(items, 10)
ten_diff_rate = calculate_util.calculate_rate(current_data[5], ten_price)
if (ten_diff_rate > float_per):
return
twenty_price = calculate_util.calculate_average(items, 20)
twenty_diff_rate = calculate_util.calculate_rate(current_data[5],
twenty_price)
if (twenty_diff_rate > float_per):
return
"""保存信息到策略记录表"""
stock_strategy_dao.stock_strategy().save_strategy_choose(
current_data, data['data']['symbol'], 5)
def get_up_wave(data):
"""回溯天数"""
call_back_day = 10
"""容错天数"""
fault_day = 2
"""计算5日线回溯天数"""
five_average_day = call_back_day + 5
"""获取最近5日数据"""
item_ = data["data"]
items = item_[-five_average_day:]
for index in range(call_back_day):
"""获取当天收盘价 多了5天计算平均值 所有位移5位 """
current_day_close_price = float(
items[index + 5].split(",")[crawl_html_url.close_price_index])
"""获取计算5日价的 天数"""
calculate_five_day = items[index + 1:index + 6]
count = 0
for every_day in calculate_five_day:
count += float(
every_day.split(",")[crawl_html_url.close_price_index])
five_average_value = round(count / 5, 2)
"""判断收盘价是否低于5日线 低于5日线 容错日减一"""
if (current_day_close_price < five_average_value):
fault_day -= 1
"""最终容错日大于-1 证明该股票符合策略"""
if (fault_day > -1):
current_day_data = items[-1]
# code = str(data['code'])
# if code.startswith("0") | code.startswith("3"):
# code = "SZ" + code
# else:
# code = "SH" + code
stock_strategy_dao.stock_strategy().save_strategy_choose(
current_day_data, data['code'], 4)
def get_biding_info(count=30, max_gain=4, min_gain=2):
session = requests.session()
session.get(crawl_html_url.snow_ball_main_url, headers=headers)
html_data = session.get(
crawl_html_url.snow_ball_stock_info_url.format(4000), headers=headers)
data = json.loads(html_data.text)
stock_list = data["data"]["list"]
result = []
sort_array = []
"""去除停盘的 停盘的没有交易量 无法比较"""
for stock in stock_list:
if (stock['volume'] != None):
sort_array.append(stock)
"""根据成交量排序"""
sorts_list = sorted(sort_array, key=lambda x: x['volume'], reverse=True)
"""获取指定排名前面的数据"""
before_list = sorts_list[0:count]
for stock in before_list:
percent = stock["percent"]
"""判断该股票涨幅是否在 指定区间内"""
if (percent > min_gain and percent < max_gain):
result.append(stock)
#持久化数据到mysql
stock_info = [
stock["name"], stock["symbol"], stock["current"],
stock["percent"], 1
]
stock_strategy_dao.stock_strategy().save_other_strategy_choose(
stock_info)
return result
def afternoon_bidding_choose():
"""根据主力资金净流入 爬取净流入最大的10条数据"""
html_data = requests.get(crawl_html_url.east_money_main_funds.format(20))
data = json.loads(html_data.text)["data"]["diff"]
for stock in data:
stock_code = complete_stock_code.complete_stock_code(stock["f12"])
stock_info = [stock["f14"], stock_code, stock["f2"], stock["f3"], 8]
stock_strategy_dao.stock_strategy().save_other_strategy_choose(
stock_info)
def get_stock_position_combination(begin_time=date_time_util.get_date_time(-1),
end_time=date_time_util.get_date_time(0),
page=1,
total=100):
begin_time_timestamp = int(
time.mktime(time.strptime(begin_time, "%Y-%m-%d %H:%M:%S"))) * 1000
end_time_timestamp = int(
time.mktime(time.strptime(end_time, "%Y-%m-%d %H:%M:%S"))) * 1000
"""禁止直接访问模拟 浏览器请求"""
"""使用会话连接 每次访问必须先访问主页"""
session = requests.session()
session.get(crawl_html_url.snow_ball_main_url, headers=headers)
"""获取真正有效数据"""
html_data = session.get(crawl_html_url.snow_ball_position_url.format(
str(page), str(total)),
headers=headers)
data = json.loads(html_data.text)
combinations_list = data['list']
result = {}
"""遍历投资者"""
for conbination in combinations_list:
"""更新时间在指定时间内的才进行查询仓位操作"""
if (conbination["updated_at"] > begin_time_timestamp
and conbination["updated_at"] < end_time_timestamp):
user_id = str(conbination["last_user_rb_gid"])
"""获取仓位数据"""
investor_html_data = session.get(
crawl_html_url.snow_ball_investor_url.format(user_id),
headers=headers)
investor_data = json.loads(investor_html_data.text)
position_list = investor_data["rebalancing"][
"rebalancing_histories"]
"""遍历仓位变动数据"""
for postion in position_list:
"""volume表示买进"""
if (postion["volume"] > 0):
stock_name = postion["stock_name"]
stock_code = postion["stock_symbol"]
bean = result.get(stock_name)
if (None == bean):
bean = snow_ball_bean.snowball_stock_info(
stock_name, stock_code, 1)
result[stock_name] = bean
else:
bean.count = bean.stock_count + 1
stock_info = [
postion["stock_name"], postion["stock_symbol"],
postion["price"], 0, 2
]
stock_strategy_dao.stock_strategy(
).save_other_strategy_choose(stock_info)
return result
def call_back_support_stock(data):
# 回溯时间
call_back_day = 60
# 排除时间周期
exclude_day = 20
# 相差幅度
float_per = 2
items = data["data"]["item"]
# 截取回溯天数数据
items = items[-call_back_day:]
"""查询结果小于回溯天数 可能是新股 跳过"""
if (len(items) < call_back_day):
return
current_day_data = items[-1]
# 获取此时最低价
current_low_price = current_day_data[4]
# 获取此时最新报价
current_new_price = current_day_data[5]
# 获取此时最低价时间
current_low_day = current_day_data[0]
# 获取此时波动幅度
current_percent = current_day_data[7]
# 获取上个高点周期数据
last_days = items[:(call_back_day - exclude_day)]
# 获取上个周期 最高价排序
sorts_list = sorted(last_days, key=lambda x: x[3], reverse=True)
# 获取上个高点价格
last_high_price = sorts_list[0][3]
# 获取上个高点时间
last_high_day = sorts_list[0][0]
# 计算两个价格之间的相差波动率
percent = (current_low_price - last_high_price) / last_high_price
percent = abs(round(percent * 100))
# 判断计算结果是否在指定区间内
if (percent < float_per):
stock_strategy_dao.stock_strategy().save_strategy_choose(
items[call_back_day - 1], data['data']['symbol'], 3)
def year_average_choose(data):
days = 250
float_per = 20
# 获取天数据
item_ = data["data"]
# 判断天数据是否低于250 天 低于250 不予处理
if len(item_) < days:
return
# 获取最近250天数据
items = item_[-days:]
current_day_close_price = float(
items[-1].split(",")[crawl_html_url.close_price_index])
count = 0
for every_day in items:
count += float(every_day.split(",")[crawl_html_url.close_price_index])
year_average_price = round(count / days, 2)
diff_per = round(((year_average_price - current_day_close_price) /
current_day_close_price) * 100, 2)
if (diff_per > 0) & (diff_per < float_per):
stock_strategy_dao.stock_strategy().save_strategy_choose(
items[-1], data['code'], 9)
def __init__(self):
self.redis = redis_pool.RedisPool()
self.log_dao = log_dao.log_record()
self.stock_strategy_record = stock_strategy_record_dao.stock_strategy_record(
)
self.stock_strategy = stock_strategy_dao.stock_strategy()
def __init__(self):
self.stock_strategy_dao = stock_strategy_dao.stock_strategy()
self.stock_strategy_record_dao = stock_strategy_record_dao.stock_strategy_record(
)
pass
