def update_current_year():
"""
"""
common.run_with_args(stat_pro_basics)
common.run_with_args(stat_fina_indicator_current)
common.run_with_args(stat_balancesheet_current)
common.run_with_args(stat_income_current)
common.run_with_args(stat_dividend_current)
def update_last_10_years():
# tmp_datetime = common.run_with_args(stat_stock_basics)
# tmp_datetime = common.run_with_args(stat_stock_profit)
# tmp_datetime = common.run_with_args(stat_stock_report)
common.run_with_args(stat_pro_basics)
common.run_with_args(stat_fina_indicator)
common.run_with_args(stat_balancesheet)
common.run_with_args(stat_income)
common.run_with_args(stat_dividend)
stock = pd.DataFrame({"close": stock["close"]}, index=stock.index.values)
stock = stock.sort_index(0) # 将数据按照日期排序下。
# print(stock.head(10))
arr = pd.Series(stock["close"].values)
# print(df_arr)
wave_mean = arr.mean()
max_point = 3 # 获得最高的几个采样点。
# 计算股票的波峰值。
wave_crest = heapq.nlargest(max_point, enumerate(arr), key=lambda x: x[1])
wave_crest_mean = pd.DataFrame(wave_crest).mean()
# 输出元祖第一个元素是index，第二元素是比较的数值 计算数据的波谷值
wave_base = heapq.nsmallest(max_point, enumerate(arr), key=lambda x: x[1])
wave_base_mean = pd.DataFrame(wave_base).mean()
# 输出数据
print("##############", len(stock))
if len(stock) > 180:
# code date wave_base wave_crest wave_mean 顺序必须一致。返回的是行数据，然后填充。
return list(
[code, date, wave_base_mean[1], wave_crest_mean[1], wave_mean])
else:
return list([code, date, 0, 0, 0])
# main函数入口
if __name__ == '__main__':
# 二次筛选数据。
tmp_datetime = common.run_with_args(stat_all_lite)
merge_sell_data = stock_sell_merge.apply(apply_merge_sell,
axis=1) # , axis=1)
# 重命名
del merge_sell_data["date"] # 合并前删除 date 字段。
# 合并数据
data_new = pd.merge(data_new, merge_sell_data, on=['code'], how='left')
# 删除老数据。
try:
del_sql = " DELETE FROM `stock_data`.`" + table_name + "` WHERE `date`= '%s' " % datetime_int
common.insert(del_sql)
print("insert_db")
except Exception as e:
print("error :", e)
del data_new["trade_float32"]
try:
common.insert_db(data_new, table_name, False, "`date`,`code`")
print("insert_db")
except Exception as e:
print("error :", e)
# 重命名
del data_new["name"]
print(data_new)
# main函数入口
if __name__ == '__main__':
# 计算买卖。
common.run_with_args(stat_index_calculate)
# print(stock["mov_vol"].tail())
# print(stock["return"].tail())
# print("stock[10d].tail(1)", stock["10d"].tail(1).values[0])
# 10d 20d 5-10d 5-20d 5d 60d code date mov_vol return
# tmp = list([stock["10d"].tail(1).values[0], stock["20d"].tail(1).values[0], stock["5-10d"].tail(1).values[0],
# stock["5-20d"].tail(1).values[0], stock["5d"].tail(1).values[0], stock["60d"].tail(1).values[0],
# code, date, stock["mov_vol"].tail(1).values[0], stock["return"].tail(1).values[0]])
# # print(tmp)
# return tmp
# main函数入口
if __name__ == '__main__':
# 使用方法传递。
tmp_datetime = common.run_with_args(stat_all_batch)
# 二次筛选数据。直接计算买卖股票数据。
tmp_datetime = common.run_with_args(stat_all_lite_buy)
tmp_datetime = common.run_with_args(stat_all_lite_sell)
####################### 老方法，弃用了。#######################
def stat_index_all_no_use(tmp_datetime):
datetime_str = (tmp_datetime).strftime("%Y-%m-%d")
datetime_int = (tmp_datetime).strftime("%Y%m%d")
print("datetime_str:", datetime_str)
print("datetime_int:", datetime_int)
# 查询今日满足股票数据。剔除数据：创业板股票数据，中小板股票数据，所有st股票
# #`code` not like '002%' and `code` not like '300%' and `name` not like '%st%'
sql_1 = """
stock["10d"] = stock["close"].rolling(window=10).mean() # 半月线
stock["20d"] = stock["close"].rolling(window=20).mean() # 月线
stock["60d"] = stock["close"].rolling(window=60).mean() # 季度线
# 计算日期差。
stock["5-10d"] = (stock["5d"] - stock["10d"]) * 100 / stock["10d"] # 周-半月线差
stock["5-20d"] = (stock["5d"] - stock["20d"]) * 100 / stock["20d"] # 周-月线差
# 计算股票的收益价格
stock["return"] = np.log(stock["close"] / stock["close"].shift(1))
# print(stock["return"])
# 计算股票的【收益率的移动历史标准差】
mov_day = int(len(stock) / 20)
# print("mov_day:", mov_day, len(stock))
stock["mov_vol"] = stock["return"].rolling(window=mov_day).std() * math.sqrt(mov_day)
# print(stock["mov_vol"].tail())
# print(stock["return"].tail())
# print("stock[10d].tail(1)", stock["10d"].tail(1).values[0])
# 10d 20d 5-10d 5-20d 5d 60d code date mov_vol return
tmp = pd.Series([stock["10d"].tail(1).values[0], stock["20d"].tail(1).values[0], stock["5-10d"].tail(1).values[0],
stock["5-20d"].tail(1).values[0], stock["5d"].tail(1).values[0], stock["60d"].tail(1).values[0],
code, date, stock["mov_vol"].tail(1).values[0], stock["return"].tail(1).values[0]],
index=['10d', '20d', '5-10d', '5-20d', '5d', '60d', 'code', 'date', 'mov_vol', 'return'])
# print(tmp)
return tmp
# main函数入口
if __name__ == '__main__':
# 使用方法传递。
common.run_with_args(stat_index_all)
#### 登陆系统 ####
lg = bs.login()
# 显示登陆返回信息
print('login respond error_code:' + lg.error_code)
print('login respond error_msg:' + lg.error_msg)
date = date.strftime("%Y-%m-%d")
print(date)
stock_rs = bs.query_all_stock(date)
stock_df = stock_rs.get_data()
print(stock_df.empty)
if not (stock_df is None or stock_df.empty):
data_df = pd.DataFrame()
for code in stock_df["code"]:
k_rs = bs.query_history_k_data_plus(
code, "date,code,open,high,low,close,preclose,volume,"
"amount,adjustflag,turn,tradestatus,pctChg,peTTM,"
"pbMRQ,psTTM,pcfNcfTTM,isST", date, date)
data_df = data_df.append(k_rs.get_data())
data = data_df.drop_duplicates(subset={"date", "code"}, keep="last")
common.insert_db(data, "bs_stock_k_data", False, "`date`,`code`")
print(date + " done!")
else:
print(date + " no data .")
bs.logout()
# main函数入口
if __name__ == '__main__':
# 使用方法传递
common.run_with_args(query_stock_k_data_plus)
# 增加季度字段。
data = concat_quarter(year, quarter, data)
# 处理重复数据，保存最新一条数据。最后一步处理，否则concat有问题。
data = data.drop_duplicates(subset="code", keep="last")
# 插入数据库。
common.insert_db(data, "ts_growth_data", False, "`quarter`,`code`")
# 偿债能力
data = ts.get_debtpaying_data(year, quarter)
# 增加季度字段。
data = concat_quarter(year, quarter, data)
# 处理重复数据，保存最新一条数据。最后一步处理，否则concat有问题。
data = data.drop_duplicates(subset="code", keep="last")
# 插入数据库。
common.insert_db(data, "ts_debtpaying_data", False, "`quarter`,`code`")
# 现金流量
data = ts.get_cashflow_data(year, quarter)
# 增加季度字段。
data = concat_quarter(year, quarter, data)
# 处理重复数据，保存最新一条数据。最后一步处理，否则concat有问题。
data = data.drop_duplicates(subset="code", keep="last")
# 插入数据库。
common.insert_db(data, "ts_cashflow_data", False, "`quarter`,`code`")
# main函数入口
if __name__ == '__main__':
# 使用方法传递。
common.run_with_args(stat_all)
# 使用缓存方法。加快计算速度。
stock = common.get_hist_data_cache(code, date_start, date_end)
stock = pd.DataFrame({"close": stock["close"]}, index=stock.index.values)
stock = stock.sort_index(0) # 将数据按照日期排序下。
# print(stock.head(10))
arr = pd.Series(stock["close"].values)
# print(df_arr)
wave_mean = arr.mean()
# 计算股票的波峰值。
wave_crest = heapq.nlargest(5, enumerate(arr), key=lambda x: x[1])
wave_crest_mean = pd.DataFrame(wave_crest).mean()
# 输出元祖第一个元素是index，第二元素是比较的数值 计算数据的波谷值
wave_base = heapq.nsmallest(5, enumerate(arr), key=lambda x: x[1])
wave_base_mean = pd.DataFrame(wave_base).mean()
# 输出数据
# print("##############")
tmp = {"code": code, "wave_mean": wave_mean,
"wave_crest": wave_crest_mean[1], "wave_base": wave_base_mean[1]}
# print(tmp)
# code date wave_base wave_crest wave_mean 顺序必须一致。返回的是行数据，然后填充。
return list([code, date, wave_base_mean[1], wave_crest_mean[1], wave_mean])
# main函数入口
if __name__ == '__main__':
# 使用方法传递。
tmp_datetime = common.run_with_args(stat_index_all)
buffett=res_data.buffett,
dividend=res_data.dividend,
positive=res_data.positive)
datetime_str = (tmp_datetime).strftime("%Y%m%d")
filename = "/data/logs/mail_%s.html" % datetime_str
with open(filename, 'w') as fout:
fout.write(res)
title = "A投资建议 %s" % datetime_str
notify_mails = MAILS
Wednesday = 2
if tmp_datetime.weekday() == Wednesday:
notify_mails += WEEK_MAILS
mail_cmd = 'mail -a "Content-type: text/html" -s "{title}" {mails} < {filename}'.format(
title=title, filename=filename, mails=' '.join(notify_mails))
import subprocess
subprocess.call(mail_cmd, shell=True)
# main函数入口
if __name__ == '__main__':
# 使用方法传递。
tmp_datetime = common.run_with_args(stat_pro_basics)
res_data = ResData()
tmp_datetime = common.run_with_args(daily_defensive, res_data)
tmp_datetime = common.run_with_args(daily_divdend, res_data)
tmp_datetime = common.run_with_args(daily_positive, res_data)
tmp_datetime = common.run_with_args(save_then_mail, res_data)
and (inventories is NULL or (total_cur_assets - inventories) / total_cur_liab > 1.0)
and ts_b.ts_code in (
select ts_code from ts_pro_fina_indicator where end_date > {half_year}0101 and end_date < {cur_year}0101 and end_date like "%%1231" and roe>5 group by ts_code having count(distinct year(end_date)) >= {half_num} and
ts_code in (
select ts_code from ts_pro_income where end_date > {cur_year_peer}0101 and end_date < {cur_year}0101 and end_date like "%%1231" and total_revenue>2010001000 group by ts_code having count(distinct year(end_date)) >= {peer_num} and
ts_code in (select ts_code from ts_pro_income where end_date > {start_year}0101 and end_date < {cur_year}0101 and end_date like "%%1231" and diluted_eps > 0 GROUP by ts_code HAVING count(distinct year(end_date)) >= {max_year}
)
)
)
) ts_balancesheet on ts_pro_basics.ts_code = ts_balancesheet.ts_code
""".format(start_year=start_year,
start_year_peer=start_year + peer_num,
cur_year=cur_year,
last_year=cur_year - 1,
cur_year_peer=cur_year - peer_num,
peer_num=peer_num,
max_year=max_year,
half_num=half_num,
half_year=cur_year - half_num)
gen_res_common("ts_res_positive", sql_pro, cur_year)
# main函数入口
if __name__ == '__main__':
# 使用方法传递。
update_current_year()
common.run_with_args(defensive_main)
common.run_with_args(buffett_main)
common.run_with_args(defensive_weak_main)
common.run_with_args(positive_main)
print("########################### stock_X_next date:", stock_X_next)
# 使用今天的交易价格，13 个指标预测明天的价格。偏移股票数据，今天的数据，目标是明天的价格。
stock_X = stock_X.drop(stock_X.index[len(stock_X) - 1]) # 删除最后一条数据
stock_y = stock_y.drop(stock_y.index[0]) # 删除第一条数据
# print("########################### stock_X date:", stock_X)
# 删除掉close 也就是收盘价格。
del stock_X["close"]
del stock_X_next["close"]
model = linear_model.LinearRegression()
# model = KNeighborsClassifier()
model.fit(stock_X.values, stock_y)
# print("############## test & target #############")
# print("############## coef_ & intercept_ #############")
# print(model.coef_) # 系数
# print(model.intercept_) # 截断
next_close = model.predict([stock_X_next.values])
if len(next_close) == 1:
next_close = next_close[0]
sklearn_score = model.score(stock_X.values, stock_y)
print("score:", sklearn_score) # 评分
return list([code, date, next_close, sklearn_score * 100])
# main函数入口
if __name__ == '__main__':
# 使用方法传递。
common.run_with_args(stat_all_batch)
print("########################### stock_X_next date:", stock_X_next)
# 使用今天的交易价格，13 个指标预测明天的价格。偏移股票数据，今天的数据，目标是明天的价格。
stock_X = stock_X.drop(stock_X.index[len(stock_X) - 1]) # 删除最后一条数据
stock_y = stock_y.drop(stock_y.index[0]) # 删除第一条数据
# print("########################### stock_X date:", stock_X)
# 删除掉close 也就是收盘价格。
del stock_X["close"]
del stock_X_next["close"]
model = linear_model.LinearRegression()
# model = KNeighborsClassifier()
model.fit(stock_X.values, stock_y)
# print("############## test & target #############")
# print("############## coef_ & intercept_ #############")
# print(model.coef_) # 系数
# print(model.intercept_) # 截断
next_close = model.predict([stock_X_next.values])
if len(next_close) == 1:
next_close = next_close[0]
sklearn_score = model.score(stock_X.values, stock_y)
print("score:", sklearn_score) # 评分
return list([code, date, next_close, sklearn_score * 100])
# main函数入口
if __name__ == '__main__':
# 使用方法传递。
tmp_datetime = common.run_with_args(stat_all_batch)
print(stock_sell_merge.head(1))
merge_sell_data = stock_sell_merge.apply(apply_merge_sell, axis=1) # , axis=1)
# 重命名
del merge_sell_data["date"] # 合并前删除 date 字段。
# 合并数据
data_new = pd.merge(data_new, merge_sell_data, on=['code'], how='left')
# 删除老数据。
try:
del_sql = " DELETE FROM `stock_data`.`" + table_name + "` WHERE `date`= '%s' " % datetime_int
common.insert(del_sql)
print("insert_db")
except Exception as e:
print("error :", e)
del data_new["trade_float32"]
try:
common.insert_db(data_new, table_name, False, "`date`,`code`")
print("insert_db")
except Exception as e:
print("error :", e)
# 重命名
del data_new["name"]
print(data_new)
# main函数入口
if __name__ == '__main__':
# 计算买卖。
tmp_datetime = common.run_with_args(stat_index_calculate)
