"""
股性分析
"""

# 是否有涨停
import copy
import json

import gpcode_manager

# 代码股性记录管理
import tool
from db.redis_manager import RedisManager


class CodeNatureRecordManager:
    __redisManager = RedisManager(0)

    @classmethod
    def __get_redis(cls):
        return cls.__redisManager.getRedis()

    # 保存K线形态
    @classmethod
    def save_k_format(cls, code, k_format):
        cls.__get_redis().setex(f"k_format-{code}", tool.get_expire(), json.dumps(k_format))

    @classmethod
    def get_k_format(cls, code):
        val = cls.__get_redis().get(f"k_format-{code}")
        if val:
            return json.loads(val)
        return None

    # 保存股性
    @classmethod
    def save_nature(cls, code, natures):
        cls.__get_redis().setex(f"code_nature-{code}", tool.get_expire(), json.dumps(natures))

    @classmethod
    def get_nature(cls, code):
        val = cls.__get_redis().get(f"code_nature-{code}")
        if val:
            return json.loads(val)
        return None


# 设置历史K线
def set_record_datas(code, limit_up_price, record_datas):
    k_format = get_k_format(limit_up_price, record_datas)
    CodeNatureRecordManager.save_k_format(code, k_format)
    natures = get_nature(record_datas)
    CodeNatureRecordManager.save_nature(code, natures)

# 获取K线形态
# 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)
def get_k_format(limit_up_price, record_datas):
    p1 = get_lowest_price_rate(record_datas) >= 0.249
    p2 = __is_new_top(limit_up_price, record_datas)
    p3 = __is_lowest(record_datas)
    p4 = __is_near_new_top(limit_up_price, record_datas)
    p5 = __is_n_model(record_datas)
    p6 = __is_v_model(record_datas)
    # N字型包含了N字型
    if p5:
        p6 = False
    return (p1, p2, p3, p4, p5, p6)


# 是否具有K线形态
def is_has_k_format(limit_up_price, record_datas):
    is_too_high, is_new_top, is_lowest, is_near_new_top, is_n, is_v = get_k_format(limit_up_price, record_datas)

    # if is_too_high:
    #     return False, "15个交易日涨幅大于24.9%"
    # if is_near_new_top:
    #     return False, "逼近前高"
    if is_new_top:
        return True, "破前高"
    if is_lowest:
        return True, "超跌补涨"
    if is_n:
        return True, "N字型"
    if is_v:
        return True, "V字形"
    return False, "不满足K线形态"


# 获取股性
# 返回（是否涨停，首板溢价率是否大于0.6）
def get_nature(record_datas):
    limit_up = is_have_limit_up(record_datas)
    premium_rate = get_limit_up_premium_rate(record_datas)
    result = (limit_up, premium_rate >= 0.6)

    return result


def get_lowest_price_rate(record_datas):
    datas = copy.deepcopy(record_datas)
    datas.sort(key=lambda x: x["bob"])
    datas = datas[-15:]
    low_price = datas[0]["close"]
    for data in datas:
        if low_price > data["close"]:
            low_price = data["close"]
    return (datas[-1]["close"] - low_price) / low_price


# 是否有涨停
def is_have_limit_up(datas):
    datas = copy.deepcopy(datas)
    for i in range(len(datas)):
        item = datas[i]
        limit_up_price = float(gpcode_manager.get_limit_up_price_by_preprice(item["pre_close"]))
        if abs(limit_up_price - item["close"]) < 0.01:
            return True
    return False


def is_have_limit_up_by_code(code):
    return False


# 是否破前高
def __is_new_top(limit_up_price, datas):
    datas = copy.deepcopy(datas)
    datas.sort(key=lambda x: x["bob"])
    datas = datas[-80:]
    max_price = 0
    for data in datas:
        if max_price < data["high"]:
            max_price = data["high"]
    if limit_up_price > max_price:
        return True
    return False


# 接近新高
def __is_near_new_top(limit_up_price, datas):
    datas = copy.deepcopy(datas)
    datas.sort(key=lambda x: x["bob"])
    datas = datas[-80:]
    max_volume = 0
    price = 0
    for data in datas:
        if max_volume < data["volume"]:
            max_volume = data["volume"]
            price = data["high"]
    print(max_volume)
    if limit_up_price < price and (price - limit_up_price) / limit_up_price < 0.03:
        return True
    return False


# 是否跌破箱体
def __is_lowest(datas):
    datas = copy.deepcopy(datas)
    datas.sort(key=lambda x: x["bob"])
    datas = datas[-80:]
    min_price = 100000
    for data in datas:
        if min_price > data["low"]:
            min_price = data["low"]
    # 近5天内的最低价
    min_price_5 = 10000
    for data in datas[-5:]:
        if min_price_5 > data["low"]:
            min_price_5 = data["low"]
    if abs(min_price_5 - min_price) / min_price < 0.015:
        return True
    return False


# N字形
def __is_n_model(datas):
    datas = copy.deepcopy(datas)
    datas.sort(key=lambda x: x["bob"])
    datas = datas[-80:]
    if len(datas) >= 6:
        max_price = 0
        min_price = 1000000
        for i in range(len(datas) - 5, len(datas)):
            item = datas[i]
            print(item)
            limit_up_price = float(gpcode_manager.get_limit_up_price_by_preprice(item["pre_close"]))
            if abs(limit_up_price - item["close"]) < 0.001 and abs(
                    limit_up_price - datas[i - 1]["close"]) >= 0.001:
                # 涨停，前一天非涨停
                max_price = item["close"]
            elif max_price > 0:
                if min_price > item["low"]:
                    min_price = item["low"]
        if max_price > min_price:
            return True
    return False


# V字形
def __is_v_model(datas):
    datas = copy.deepcopy(datas)
    datas.sort(key=lambda x: x["bob"])
    datas = datas[-30:]
    max_price = 0
    max_price_index = -1
    for i in range(0, len(datas)):
        if max_price < datas[i]["close"]:
            max_price = datas[i]["close"]
            max_price_index = i
    min_price = max_price
    min_price_index = max_price_index
    for i in range(max_price_index, len(datas)):
        if min_price > datas[i]["close"]:
            min_price = datas[i]["close"]
            min_price_index = i

    if (max_price - min_price) / max_price > 0.249:
        return True

    return False


# 首板涨停溢价率
def get_limit_up_premium_rate(datas):
    datas = copy.deepcopy(datas)
    datas.sort(key=lambda x: x["bob"])
    first_rate_list = []
    for i in range(0, len(datas)):
        item = datas[i]
        limit_up_price = float(gpcode_manager.get_limit_up_price_by_preprice(item["pre_close"]))
        if abs(limit_up_price - item["close"]) < 0.001 and abs(
                limit_up_price - datas[i - 1]["close"]) >= 0.001 and 0 < i < len(datas) - 1:
            # 首板涨停
            rate = (datas[i + 1]["high"] - datas[i + 1]["pre_close"]) / datas[i + 1]["pre_close"]
            first_rate_list.append(rate)
    if not first_rate_list:
        return 1
    count = 0
    for rate in first_rate_list:
        if rate >= 0.01:
            count += 1
    return count / len(first_rate_list)