"""
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股性分析
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"""
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# 是否有涨停
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import copy
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import json
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import gpcode_manager
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# 代码股性记录管理
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import tool
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from db.redis_manager import RedisManager
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class CodeNatureRecordManager:
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__redisManager = RedisManager(0)
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@classmethod
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def __get_redis(cls):
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return cls.__redisManager.getRedis()
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# 保存K线形态
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@classmethod
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def save_k_format(cls, code, k_format):
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cls.__get_redis().setex(f"k_format-{code}", tool.get_expire(), json.dumps(k_format))
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@classmethod
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def get_k_format(cls, code):
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val = cls.__get_redis().get(f"k_format-{code}")
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if val:
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return json.loads(val)
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return None
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# 保存股性
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@classmethod
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def save_nature(cls, code, natures):
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cls.__get_redis().setex(f"code_nature-{code}", tool.get_expire(), json.dumps(natures))
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@classmethod
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def get_nature(cls, code):
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val = cls.__get_redis().get(f"code_nature-{code}")
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if val:
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return json.loads(val)
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return None
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# 设置历史K线
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def set_record_datas(code, limit_up_price, record_datas):
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k_format = get_k_format(limit_up_price, record_datas)
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CodeNatureRecordManager.save_k_format(code, k_format)
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natures = get_nature(record_datas)
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CodeNatureRecordManager.save_nature(code, natures)
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# 获取K线形态
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# 返回 (15个交易日涨幅是否大于24.9%,是否破前高，是否超跌，是否接近前高，是否N,是否V)
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def get_k_format(limit_up_price, record_datas):
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p1 = get_lowest_price_rate(record_datas) >= 0.249
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p2 = __is_new_top(limit_up_price, record_datas)
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p3 = __is_lowest(record_datas)
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p4 = __is_near_new_top(limit_up_price, record_datas)
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p5 = __is_n_model(record_datas)
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p6 = __is_v_model(record_datas)
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# N字型包含了N字型
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if p5:
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p6 = False
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return (p1, p2, p3, p4, p5, p6)
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# 是否具有K线形态
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def is_has_k_format(limit_up_price, record_datas):
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is_too_high, is_new_top, is_lowest, is_near_new_top, is_n, is_v = get_k_format(limit_up_price, record_datas)
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# if is_too_high:
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# return False, "15个交易日涨幅大于24.9%"
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# if is_near_new_top:
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# return False, "逼近前高"
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if is_new_top:
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return True, "破前高"
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if is_lowest:
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return True, "超跌补涨"
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if is_n:
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return True, "N字型"
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if is_v:
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return True, "V字形"
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return False, "不满足K线形态"
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# 获取股性
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# 返回（是否涨停，首板溢价率是否大于0.6）
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def get_nature(record_datas):
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limit_up = is_have_limit_up(record_datas)
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premium_rate = get_limit_up_premium_rate(record_datas)
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result = (limit_up, premium_rate >= 0.6)
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return result
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def get_lowest_price_rate(record_datas):
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datas = copy.deepcopy(record_datas)
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datas.sort(key=lambda x: x["bob"])
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datas = datas[-15:]
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low_price = datas[0]["close"]
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for data in datas:
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if low_price > data["close"]:
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low_price = data["close"]
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return (datas[-1]["close"] - low_price) / low_price
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# 是否有涨停
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def is_have_limit_up(datas):
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datas = copy.deepcopy(datas)
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for i in range(len(datas)):
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item = datas[i]
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limit_up_price = float(gpcode_manager.get_limit_up_price_by_preprice(item["pre_close"]))
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if abs(limit_up_price - item["close"]) < 0.01:
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return True
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return False
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def is_have_limit_up_by_code(code):
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return False
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# 是否破前高
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def __is_new_top(limit_up_price, datas):
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datas = copy.deepcopy(datas)
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datas.sort(key=lambda x: x["bob"])
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datas = datas[-80:]
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max_price = 0
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for data in datas:
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if max_price < data["high"]:
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max_price = data["high"]
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if limit_up_price > max_price:
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return True
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return False
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# 接近新高
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def __is_near_new_top(limit_up_price, datas):
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datas = copy.deepcopy(datas)
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datas.sort(key=lambda x: x["bob"])
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datas = datas[-80:]
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max_volume = 0
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price = 0
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for data in datas:
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if max_volume < data["volume"]:
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max_volume = data["volume"]
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price = data["high"]
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print(max_volume)
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if limit_up_price < price and (price - limit_up_price) / limit_up_price < 0.03:
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return True
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return False
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# 是否跌破箱体
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def __is_lowest(datas):
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datas = copy.deepcopy(datas)
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datas.sort(key=lambda x: x["bob"])
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datas = datas[-80:]
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min_price = 100000
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for data in datas:
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if min_price > data["low"]:
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min_price = data["low"]
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# 近5天内的最低价
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min_price_5 = 10000
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for data in datas[-5:]:
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if min_price_5 > data["low"]:
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min_price_5 = data["low"]
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if abs(min_price_5 - min_price) / min_price < 0.015:
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return True
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return False
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# N字形
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def __is_n_model(datas):
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datas = copy.deepcopy(datas)
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datas.sort(key=lambda x: x["bob"])
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datas = datas[-80:]
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if len(datas) >= 6:
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max_price = 0
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min_price = 1000000
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for i in range(len(datas) - 5, len(datas)):
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item = datas[i]
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print(item)
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limit_up_price = float(gpcode_manager.get_limit_up_price_by_preprice(item["pre_close"]))
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if abs(limit_up_price - item["close"]) < 0.001 and abs(
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limit_up_price - datas[i - 1]["close"]) >= 0.001:
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# 涨停，前一天非涨停
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max_price = item["close"]
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elif max_price > 0:
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if min_price > item["low"]:
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min_price = item["low"]
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if max_price > min_price:
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return True
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return False
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# V字形
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def __is_v_model(datas):
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datas = copy.deepcopy(datas)
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datas.sort(key=lambda x: x["bob"])
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datas = datas[-30:]
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max_price = 0
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max_price_index = -1
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for i in range(0, len(datas)):
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if max_price < datas[i]["close"]:
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max_price = datas[i]["close"]
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max_price_index = i
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min_price = max_price
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min_price_index = max_price_index
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for i in range(max_price_index, len(datas)):
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if min_price > datas[i]["close"]:
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min_price = datas[i]["close"]
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min_price_index = i
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if (max_price - min_price) / max_price > 0.249:
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return True
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return False
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# 首板涨停溢价率
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def get_limit_up_premium_rate(datas):
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datas = copy.deepcopy(datas)
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datas.sort(key=lambda x: x["bob"])
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first_rate_list = []
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for i in range(0, len(datas)):
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item = datas[i]
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limit_up_price = float(gpcode_manager.get_limit_up_price_by_preprice(item["pre_close"]))
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if abs(limit_up_price - item["close"]) < 0.001 and abs(
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limit_up_price - datas[i - 1]["close"]) >= 0.001 and 0 < i < len(datas) - 1:
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# 首板涨停
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rate = (datas[i + 1]["high"] - datas[i + 1]["pre_close"]) / datas[i + 1]["pre_close"]
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first_rate_list.append(rate)
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if not first_rate_list:
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return 1
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count = 0
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for rate in first_rate_list:
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if rate >= 0.01:
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count += 1
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return count / len(first_rate_list)
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