def st():
db_client = MongoDBClient(
config.get("db").get("mongodb"),
config.get("db").get("database"))
st_df = pd.read_csv(config.get("files").get("st"), header=0)
st_df['ticker'] = st_df['ticker'].map(lambda x: str(x).zfill(6))
for index, row in st_df.iterrows():
_date = str(row["tradeDate"]).replace('-', '')
_market = 0
if str(row["exchangeCD"]) == "XSHG":
_market = 1
db_client.upsert_one(_filter={
"code": str(row["ticker"]),
"market": _market,
"date": _date
},
_value={
"st": 1,
"name": "%s" % row['tradeAbbrName']
},
_upsert=False)
print("更新记录: %d" % index)
def plot1(years, money):
tax = 0.00025 # 买卖按照万分之2.5计算
for item in config.get("indexes"):
_market = item[0]
_idx_code = item[1]
_money_per = money
click.echo("开始计算 %d - %s 指数的回测策略" % (_market, _idx_code))
_start_date = date.datetime_to_str(date.years_ago(years))
last_day_list = date.last_days(int(_start_date), 1,
True) # 每月最后一个交易日集合
data = []
db_client = MongoDBClient(
config.get("db").get("mongodb"),
config.get("db").get("database"))
_idx_list = db_client.find_stock_list(_filter={
"code": _idx_code,
"market": _market
},
_sort=[("date",
pymongo.ASCENDING)],
_fields={
"date": 1,
"close": 1
})
item_df = pd.DataFrame(list(_idx_list), columns=["date", "close"])
item_df = item_df.dropna(0)
total_count = int(0) # 累计数量
for idx, item_date in enumerate(last_day_list):
last_df = item_df[item_df["date"] == str(item_date)]
if last_df is None or len(last_df) <= 0:
continue
item_point = last_df.ix[last_df.index.values[0]]["close"]
item_count = _money_per * (1 - tax) / item_point
total_count += item_count
data.append([item_date, "买入", item_point, item_count, total_count])
item = data.pop(-1)
item_count = total_count * item[2] * (1 - tax) / item[2]
data.append([item[0], "卖出", item[2], item_count, 0])
result_df = pd.DataFrame(data,
columns=["日期", "操作", "单价", "数量", "累计数量"])
result_df.to_csv(config.get("files").get("plots") %
(PROJECT_ROOT, "plot1", _market, _idx_code),
index=False,
mode="w",
header=True,
encoding='gb18030',
float_format="%.3f")
def _indexes(idx_market, idx_code, start_date, calendar_df):
db_client = MongoDBClient(
config.get("db").get("mongodb"),
config.get("db").get("database"))
today = int("".join(time.strftime('%Y%m%d', time.localtime(time.time()))))
item_last = db_client.find_stock_item(_filter={
"code": idx_code,
"market": idx_market,
"dr": {
"$exists": True
}
},
_sort=[("date", pymongo.DESCENDING)])
if item_last is not None and len(item_last) > 0:
calendar_df = calendar_df[
calendar_df["calendarDate"] > int(item_last.get("date"))]
click.echo("\t开始 %s 指数的估值信息..." % idx_code)
for _, row in calendar_df.iterrows():
cal_date = int(row["calendarDate"])
if row['isOpen'] == 0 or cal_date < start_date or cal_date > today:
continue
click.echo("\t\t计算%d-%s - %d ..." % (idx_market, idx_code, cal_date))
_result = index.index_val(cal_date, idx_code, db_client)
if _result is not None:
click.echo("\t\t\t code:%s, pe_ttm:%s, pb:%s" %
(idx_code, _result[0], _result[1]))
db_client.upsert_one(_filter={
"code": str(idx_code),
"market": idx_market,
"date": str(cal_date)
},
_value={
"pe_ttm": _result[0],
"pb": _result[1],
"roe": _result[2],
"dr": _result[3]
})
click.echo("\t%s 指数的估值信息计算完毕" % idx_code)
def a():
"""计算A股PE中值"""
click.echo("开始计算整个A股的估值信息...")
first = True
result_list = []
db_client = MongoDBClient(
config.get("db").get("mongodb"),
config.get("db").get("database"))
calendar_df = pd.read_csv(config.get("files").get("calendar"), header=0)
today = int("".join(time.strftime('%Y%m%d', time.localtime(time.time()))))
try:
stock_df = pd.read_csv(config.get("files").get("stock_a"),
header=0,
encoding="utf8")
if len(stock_df) > 0:
first = False
item_last = stock_df.loc[stock_df.index[-1]]
calendar_df = calendar_df[
calendar_df["calendarDate"] > int(item_last["date"])]
except FileNotFoundError:
pass
for _, row in calendar_df.iterrows():
cal_date = int(row["calendarDate"])
if row['isOpen'] == 0 or 20020328 > cal_date or cal_date > today:
continue
stock_day_list = db_client.find_stock_list(_filter={
"date": str(cal_date),
"fixed": {
"$exists": True
}
},
_sort=[("code",
pymongo.ASCENDING)],
_fields={
"market": 1,
"code": 1,
"close": 1,
"pe_ttm": 1,
"pb": 1,
"roe": 1,
"dr": 1
})
stock_df = pd.DataFrame(
stock_day_list,
columns=['market', 'code', 'close', 'pe_ttm', 'pb', 'roe', 'dr'])
stock_df = stock_df.fillna(0)
pe_list = []
pb_list = []
roe_list = []
dr_list = []
for _, r in stock_df.iterrows():
pe_list.append(r['pe_ttm'])
pb_list.append(r['pb'])
roe_list.append(r['roe'])
dr_list.append(r['dr'])
mid_value_pe = number.get_median(pe_list)
mid_value_pb = number.get_median(pb_list)
avg_value_roe = number.get_average(roe_list)
avg_value_dr = number.get_average(dr_list)
click.echo("计算 %s 日的数据中值, pe=%f, pb=%f, roe=%f, dr=%f ... " %
(cal_date, mid_value_pe, mid_value_pb, avg_value_roe,
avg_value_dr))
result_list.append([
cal_date, mid_value_pe, mid_value_pb, avg_value_roe, avg_value_dr
])
if len(result_list) % 50 == 0:
if first:
first = False
result_df = pd.DataFrame(
result_list, columns=['date', 'pe', 'pb', 'roe', 'dr'])
result_df.to_csv(config.get("files").get("stock_a"),
index=False,
mode="w",
header=True,
encoding='utf8',
float_format="%.6f")
else:
result_df = pd.DataFrame(
result_list, columns=['date', 'pe', 'pb', 'roe', 'dr'])
result_df.to_csv(config.get("files").get("stock_a"),
index=False,
mode="a+",
header=False,
encoding='utf8',
float_format="%.6f")
result_list = []
result_df = pd.DataFrame(result_list,
columns=['date', 'pe', 'pb', 'roe', 'dr'])
result_df.to_csv(config.get("files").get("stock_a"),
index=False,
mode="a+",
header=False,
encoding='utf8',
float_format="%.6f")
click.echo("整个A股的估值信息计算完毕...")
def plot2(years, money):
"""
红利指数定投进阶方案
80% <= pe百分位: 全卖
60% <= pe百分位 < 80%: 定赎回
40% <= pe百分位 < 60%: 正常定投
20% <= pe百分位 < 40%: 1.5倍定投
pe百分位 < 20%: 2倍定投
:return:
"""
tax = 0.00025 # 买卖按照万分之2.5计算
for item in config.get("indexes"):
data = []
_market = item[0]
_idx_code = item[1]
_money_per = money
click.echo("开始计算 %d - %s 指数的回测策略" % (_market, _idx_code))
_start_date = date.datetime_to_str(date.years_ago(years))
last_day_list = date.last_days(int(_start_date), 1,
True) # 每月最后一个交易日集合
db_client = MongoDBClient(
config.get("db").get("mongodb"),
config.get("db").get("database"))
_idx_list = db_client.find_stock_list(_filter={
"code": _idx_code,
"market": _market
},
_sort=[("date",
pymongo.ASCENDING)],
_fields={
"date": 1,
"pe_ttm": 1,
"close": 1
})
item_df = pd.DataFrame(list(_idx_list),
columns=["date", "pe_ttm", "close"])
item_df = item_df.dropna(0)
_pe_series = 1 / item_df["pe_ttm"]
item_df.insert(0, 'rank', _pe_series.rank(pct=True))
item_df = item_df.reset_index()
total_count = 0 # 累计数量
for idx, item_date in enumerate(last_day_list):
last_df = item_df[item_df["date"] == str(item_date)]
if last_df is None or len(last_df) <= 0:
continue
_tmp_rank = last_df.ix[last_df.index.values[0]]["rank"]
item_point = last_df.ix[last_df.index.values[0]]["close"]
item_rank = float("%.2f" % ((1 - _tmp_rank) * 100))
if item_rank >= 80 and total_count > 0:
# 全部卖出, 卖出后要扣税费
item_count = total_count * item_point * (1 - tax) / item_point
data.append(
[item_date, item_rank, "卖出", -item_point, item_count, 0])
total_count = 0
elif 60 <= item_rank < 80:
# 卖出1份
_op = "卖出"
# 卖出的这一份应该包含税费，所以要多卖出一部分税费的数量
item_count = _money_per * (1 + tax) / item_point
if total_count >= item_count:
total_count -= item_count
elif total_count > 0:
item_count = total_count
total_count = 0
else:
_op = "/"
item_count = 0
total_count = 0
data.append([
item_date, item_rank, _op, -item_point, item_count,
total_count
])
elif 40 <= item_rank < 60:
# 买入1份
item_count = _money_per * (1 - tax) / item_point
total_count += item_count
data.append([
item_date, item_rank, "买入", item_point, item_count,
total_count
])
elif 20 <= item_rank < 40:
# 买入1.5份
item_count = (_money_per * 1.5) * (1 - tax) / item_point
total_count += item_count
data.append([
item_date, item_rank, "买入", item_point, item_count,
total_count
])
elif 0 <= item_rank < 20:
# 买入2份
item_count = (_money_per * 2) * (1 - tax) / item_point
total_count += item_count
data.append([
item_date, item_rank, "买入", item_point, item_count,
total_count
])
else:
data.append(
[item_date, item_rank, "/", item_point, 0, total_count])
result_df = pd.DataFrame(
data, columns=["日期", "百分位", "操作", "单价", "数量", "累计数量"])
result_df.to_csv(config.get("files").get("plots") %
(PROJECT_ROOT, "plot2", _market, _idx_code),
index=False,
mode="w",
header=True,
encoding='gb18030',
float_format="%.3f")
def mv():
"""
总市值=当前股价×总股本
"""
click.echo("计算总股本、流通股本、流通市值、总市值、股息率...")
db_client = MongoDBClient(
config.get("db").get("mongodb"),
config.get("db").get("database"))
base_df = stocks.stock_a_list()
bonus_df = bonus.bonus_with(None)
report_df = pd.read_csv(config.get("files").get("reports"),
header=0,
encoding="utf8")
report_df['code'] = report_df['code'].map(lambda x: str(x).zfill(6))
for index, row in base_df.iterrows():
try:
print("计算 %d-%s 的股本、市值、股息率" % (row["market"], row["code"]))
ccs = 0 # 流通股
tcs = 0 # 总股本
stock_day_list = db_client.find_stock_list(_filter={
"code": row['code'],
"market": row["market"],
"close": {
"$exists": True
}
},
_sort=[
("date",
pymongo.ASCENDING)
],
_fields={
"date": 1,
"close": 1
})
if len(stock_day_list) <= 0:
# 股票本身没有交易量
continue
stock_day_df = pd.DataFrame(stock_day_list,
columns=['date', 'close'])
try:
item_last = db_client.find_stock_item(_filter={
"code": row['code'],
"market": row["market"],
"dr": {
"$exists": True
}
},
_sort=[
("date",
pymongo.DESCENDING)
])
if item_last is not None and len(item_last) > 0:
# 如果之前计算过，则无需重头计算
last_day_df = stock_day_df[stock_day_df["date"] ==
item_last['date']]
stock_day_df = stock_day_df[
stock_day_df.index > last_day_df.index.values[0]]
ccs = item_last["cmv"] // item_last["close"] # 流通股
tcs = item_last["tmv"] // item_last["close"] # 总股本
except FileNotFoundError:
pass
filter_bonus_df = bonus_df[(bonus_df["code"] == row["code"])
& (bonus_df["type"] != 6) &
((bonus_df["type"] > 1) &
(bonus_df["type"] < 13))]
filter_bonus_df = filter_bonus_df.sort_values(['date'],
ascending=True)
filter_report_df = report_df[report_df["code"] == row["code"]]
filter_report_df = filter_report_df.sort_values(['date'],
ascending=True)
for idx, item in stock_day_df.iterrows():
item_df = filter_bonus_df[filter_bonus_df["date"] == int(
item["date"])]
if len(item_df) > 0:
# 高送转中记录的数据单位都到万
ccs = item_df.ix[item_df.index.values[0]]["count"] * 10000
tcs = item_df.ix[item_df.index.values[0]]["rate"] * 10000
item_df = filter_report_df[filter_report_df["publish"] == int(
item["date"])]
if len(item_df) > 0:
ccs = item_df.ix[item_df.index.values[0]]["ccs"]
tcs = item_df.ix[item_df.index.values[0]]["tcs"]
ccs_mv = item["close"] * ccs
tcs_mv = item["close"] * tcs
# 3年的股息率
dr = bonus.dividend_rate_with(int(item["date"]), row["code"],
item["close"])
db_client.upsert_one(_filter={
"code": str(row["code"]),
"market": row["market"],
"date": str(item["date"])
},
_value={
"cmv": ccs_mv,
"tmv": tcs_mv,
"dr": dr
})
except FileNotFoundError:
continue
click.echo("总股本、流通股本、流通市值、总市值、股息率 计算完毕...")
def pe_pb_roe():
"""
转换PE、PB、ROE、股息率 四个指标需要的信息
1.基本每股收益、4.每股净资产、96.归属于母公司所有者的净利润、238.总股本、239.已上市流通A股
PE=股价/每股收益
PB=股价/每股净资产
ROE=利润/每股净资产=PB/PE : 财报中已有静态的净资产收益率数据, 这里通过TTM计算一个大概的ROE作为参考
:return:
"""
db_client = MongoDBClient(
config.get("db").get("mongodb"),
config.get("db").get("database"))
base_df = stocks.stock_a_list()
for index, row in base_df.iterrows():
try:
print("计算 %d-%s 的PE、PB、ROE数据..." % (row["market"], row["code"]))
stock_day_list = db_client.find_stock_list(_filter={
"code": row['code'],
"market": row["market"],
"close": {
"$exists": True
}
},
_sort=[
("date",
pymongo.ASCENDING)
],
_fields={
"date": 1,
"close": 1
})
if len(stock_day_list) <= 0:
# 股票本身没有交易量
continue
stock_day_df = pd.DataFrame(stock_day_list,
columns=['date', 'close'])
item_last = db_client.find_stock_item(_filter={
"code": row['code'],
"market": row["market"],
"pb": {
"$exists": True
}
},
_sort=[("date",
pymongo.DESCENDING)])
if item_last is not None and len(item_last) > 0:
# 如果之前计算过，则无需重头计算
last_day_df = stock_day_df[stock_day_df["date"] ==
item_last['date']]
stock_day_df = stock_day_df[
stock_day_df.index > last_day_df.index.values[0]]
for idx, item in stock_day_df.iterrows():
lyr_value = rt.lyr_with(int(item["date"]), row['code'],
item["close"])
pe_value = rt.ttm_with(int(item["date"]), row['code'],
item["close"])
pb_value = rt.pb_with(int(item["date"]), row['code'],
item["close"])
roe_value = 0.0
if pe_value != 0.0:
roe_value = pb_value / pe_value
db_client.upsert_one(_filter={
"code": str(row["code"]),
"market": row["market"],
"date": str(item["date"])
},
_value={
"lyr": lyr_value,
"pe_ttm": pe_value,
"pb": pb_value,
"roe": roe_value
})
except FileNotFoundError:
continue
def fixed():
db_client = MongoDBClient(
config.get("db").get("mongodb"),
config.get("db").get("database"))
base_df = stocks.stock_a_list()
bonus_df = bonus.bonus_with(None)
for index, row in base_df.iterrows():
try:
prev_close = 0
prev_fixed_close = 0
print("计算 %d-%s 的后复权数据" % (row["market"], row["code"]))
stock_day_list = db_client.find_stock_list(_filter={
"code": row['code'],
"market": row["market"]
},
_sort=[
("date",
pymongo.ASCENDING)
],
_fields={
"date": 1,
"close": 1
})
if len(stock_day_list) <= 0:
# 股票本身没有交易量无需复权
continue
stock_day_df = pd.DataFrame(stock_day_list,
columns=['date', 'close'])
try:
item_last = db_client.find_stock_item(_filter={
"code": row['code'],
"market": row["market"],
"fixed": {
"$exists": True
}
},
_sort=[
("date",
pymongo.DESCENDING)
])
if item_last is not None and len(item_last) > 0:
# 如果之前计算过后复权价，则无需重头计算
last_day_df = stock_day_df[stock_day_df["date"] ==
item_last['date']]
prev_close = last_day_df.ix[
last_day_df.index.values[0]]["close"]
prev_fixed_close = item_last["fixed"]
stock_day_df = stock_day_df[
stock_day_df.index > last_day_df.index.values[0]]
except FileNotFoundError:
# 如果从来没计算过后复权价则不管
pass
filter_df = bonus_df[(bonus_df["type"] == 1)
& (bonus_df["code"] == row["code"])]
filter_df = filter_df.sort_values(['date'], ascending=True)
for idx, item in stock_day_df.iterrows():
money = 0 # 分红
count = 0 # 送股数
item_df = filter_df[filter_df["date"] == int(item["date"])]
if len(item_df) > 0:
money = item_df.ix[item_df.index.values[0]]["money"] / 10
count = item_df.ix[item_df.index.values[0]]["count"] / 10
# 除息除权日当天复权后的涨幅 =（当天不复权收盘价 *（1 + 每股送股数量）+每股分红金额） / 上一个交易日的不复权收盘价
# 复权收盘价 = 上一个交易日的复权收盘价 *（1 + 复权涨幅)
if prev_close > 0:
daily_rate_close = (item["close"] *
(1 + count) + money) / prev_close
prev_fixed_close = prev_fixed_close * daily_rate_close
else:
prev_fixed_close = item["close"]
prev_close = item["close"]
db_client.upsert_one(_filter={
"code": str(row["code"]),
"market": row["market"],
"date": str(item["date"])
},
_value={"fixed": prev_fixed_close})
except FileNotFoundError:
continue