def sh50_buy_worst_ma(engine, start_day, end_day, max_hold, howlong, base_code,
base_name): # N日相对均线最遭
#从DB抓日线数据
conn = engine.connect()
# 获取日线数据
# 返回数组
# T_day1, {证券1:证券1的行情, 证券2:证券2的行情, ... }
# T_day2, {证券1:证券1的行情, 证券2:证券2的行情, ... }
# T_day3, {证券1:证券1的行情, 证券2:证券2的行情, ... }
# ...
# 其中‘行情’ 是 [收盘价，前日收盘，涨幅， 涨停标志，停牌标志]
his_md = db_operator.db_fetch_dailyline(conn, start_day)
# 在日线数据中，扩充加入指标数据
# 返回时，数组md_his_data扩充为
# T_day1, {证券1:证券1的行情, 证券2:证券2的行情, ... }, {证券1:证券1的行情, 证券2:证券2的行情, ... }
# T_day2, {证券1:证券1的行情, 证券2:证券2的行情, ... }, {证券1:证券1的行情, 证券2:证券2的行情, ... }
# T_day3, {证券1:证券1的行情, 证券2:证券2的行情, ... }, {证券1:证券1的行情, 证券2:证券2的行情, ... }
# ...
# ‘指标’ 是 [可买标志，均线偏离度， 均线]
make_indices_by_MA_delta(conn, his_md, howlong)
result, trans_num, trans_cost = sim_rotate_buy_worst(
his_md, max_hold, howlong, base_code, start_day, end_day)
# Output: 2-D array , 交易数， 交易成本
# 日期 基准收盘价 策略净值 交易次数 换仓详细
# ...
#
#util.bp( result)
#准备画图
#chart_head = ['日期', '50指数', 'MA10' ]
#chart_data=[]
#for entry in his_md:
# row = [ entry[0], entry[1][FH_BASE_CODE][0] , entry[2][FH_BASE_CODE][2] ]
# chart_data.append(row)
#plotter.simple_generate_line_chart( chart_head, chart_data)
base_info = data_struct.SecurityInfo()
base_info.code = base_code
base_info.name = base_name
secs = [base_info]
suffix = ".from_%s" % start_day
plotter.generate_htm_chart_for_faster_horse2(secs, result, suffix)
#show summary
t_day_num = len(result)
base_delta = result[t_day_num - 1][1] / result[0][1]
policy_delta = result[t_day_num - 1][2] / result[0][2]
print "%d日最糟, %s ~ %s, %d个交易日，交易%d笔，交易成本%f，基准表现%f，策略表现%f" % (
howlong, result[0][0], result[t_day_num - 1][0], t_day_num, trans_num,
trans_cost, base_delta, policy_delta)
def bt_brk_pb_policy(engine, start_day, end_day,max_hold, threshold ):
#从DB抓日线数据
conn = engine.connect()
# 获取日线数据
# 返回数组
# T_day1, {证券1:证券1的行情, 证券2:证券2的行情, ... }
# T_day2, {证券1:证券1的行情, 证券2:证券2的行情, ... }
# T_day3, {证券1:证券1的行情, 证券2:证券2的行情, ... }
# ...
# 其中‘行情’ 是 [收盘价，前日收盘，涨幅， 涨停标志，停牌标志, PB, 换手]
his_md = db_operator. db_fetch_dailyline_w_valuation(conn, threshold )
# 在日线数据中，扩充加入指标数据
# 返回时，数组md_his_data扩充为
# T_day1, {证券1:证券1的行情, 证券2:证券2的行情, ... }, {证券1:证券1的指标, 证券2:证券2的指标, ... }
# T_day2, {证券1:证券1的行情, 证券2:证券2的行情, ... }, {证券1:证券1的指标, 证券2:证券2的指标, ... }
# T_day3, {证券1:证券1的行情, 证券2:证券2的行情, ... }, {证券1:证券1的指标, 证券2:证券2的指标, ... }
# ...
# ‘指标’ 是 [可买标志，PB偏离度, PB, N日PB平均，N日PB标准差]
make_indices_by_pb_standard_deviation( conn, his_md , threshold)
result, trans_num, trans_cost = sim_brk_pb_policy( conn,his_md, max_hold , BRK_INDEX, start_day, end_day )
# Output: 2-D array , 交易数， 交易成本
# 日期 基准收盘价 策略净值 交易次数 换仓详细
# ...
#
#util.bp( result)
#准备画图
#chart_head = ['日期', '50指数', 'MA10' ]
#chart_data=[]
#for entry in his_md:
# row = [ entry[0], entry[1][FH_BASE_CODE][0] , entry[2][FH_BASE_CODE][2] ]
# chart_data.append(row)
#plotter.simple_generate_line_chart( chart_head, chart_data)
base_info = data_struct.SecurityInfo()
base_info.code = BRK_INDEX
base_info.name = BRK_INDEX_NAME
secs = [ base_info ]
suffix = ".from_%s" % start_day
plotter.generate_htm_chart_for_faster_horse2( secs, result , suffix)
#show summary
t_day_num = len(result)
base_delta = result[ t_day_num - 1][1] / result[ 0][1]
policy_delta = result[ t_day_num - 1][2] / result[ 0][2]
print "券商PB策略(%d日), %s ~ %s, %d个交易日，交易%d笔，交易成本%f，基准表现%f，策略表现%f" % (
threshold
, result[0][0], result[ t_day_num - 1][0], t_day_num
, trans_num, trans_cost
, base_delta, policy_delta
)
def fh50_until_now(engine, start_year):
now = datetime.now()
#从DB抓日线数据
start_day = "%d-01-01" % start_year
conn = engine.connect()
# 获取日线数据
# 返回数组
# T_day1, {证券1:证券1的行情, 证券2:证券2的行情, ... }
# T_day2, {证券1:证券1的行情, 证券2:证券2的行情, ... }
# T_day3, {证券1:证券1的行情, 证券2:证券2的行情, ... }
# ...
# 其中‘行情’ 是 [收盘价，前日收盘，涨幅， 涨停标志，停牌标志]
his_md = db_operator.db_fetch_dailyline(conn, start_day)
# 在日线数据中，扩充加入指标数据
# 返回时，数组md_his_data扩充为
# T_day1, {证券1:证券1的行情, 证券2:证券2的行情, ... }, {证券1:证券1的行情, 证券2:证券2的行情, ... }
# T_day2, {证券1:证券1的行情, 证券2:证券2的行情, ... }, {证券1:证券1的行情, 证券2:证券2的行情, ... }
# T_day3, {证券1:证券1的行情, 证券2:证券2的行情, ... }, {证券1:证券1的行情, 证券2:证券2的行情, ... }
# ...
# ‘指标’ 是 [可买标志，三日累计涨幅]
make_indices_by_delta(conn, his_md)
#util.bp_as_json( his_md)
#util.bp( his_md)
result, trans_num, trans_cost = sim_rotate(his_md, 3, FH_BASE_CODE,
start_day, end_day)
# Output: 2-D array , 交易数， 交易成本
# 日期 基准收盘价 策略净值 交易次数 换仓详细
# ...
#
#util.bp( result)
#准备画图
base_info = data_struct.SecurityInfo()
base_info.code = FH_BASE_CODE
base_info.name = FH_BASE_NAME
secs = [base_info]
suffix = ".from_%d" % start_year
plotter.generate_htm_chart_for_faster_horse2(secs, result, suffix)
#show summary
t_day_num = len(result)
base_delta = result[t_day_num - 1][1] / result[0][1]
policy_delta = result[t_day_num - 1][2] / result[0][2]
print "%s ~ %s, %d个交易日，交易%d笔，交易成本%f，基准表现%f，策略表现%f" % (
result[0][0], result[t_day_num - 1][0], t_day_num, trans_num,
trans_cost, base_delta, policy_delta)
def load_md_his(filepath ,dbcur, inventory_ranges ):
the_file = io.open( filepath, "r", encoding='utf-8')
reader = csv.DictReader( the_file, dialect = 'excel-tab')
#reader = csv.reader( the_file, dialect = 'excel-tab')
row_num = 0
info = data_struct.SecurityInfo( )
info.parse_from_filepath( filepath )
the_code = info.code
#info.dump()
db_operator.save_sec_info_to_db(dbcur, info)
if the_code in inventory_ranges:
the_range = inventory_ranges[the_code]
else:
the_range = None
#print the_range
for row in reader:
row_num +=1
if 1== row_num:
verify_his_csv_format(filepath, row )
#s = str(row).decode('string_escape').decode('utf8') # 能显示 DectReader吐的中文
#s = row[0].decode('utf8') #<= OK，能显示 plain reader 吐的
#.decode('string_escape')
#print s
#pdb.set_trace()
md_record = data_struct.MdRecord()
md_record.code = the_code
md_record.load_from_his_csv_row( row)
#md_record.dump()
if the_range is not None \
and md_record.t_day >= the_range.start \
and md_record.t_day <= the_range.end :
continue
db_operator.save_MD_to_db( dbcur, md_record)
print "%s was imported" % filepath
the_file.close()
def get_sec_info(dbcur):
dbcur.execute('''
select code, name, dir
from SecurityInfo
''')
r = {}
row = dbcur.fetchone()
while row is not None:
one_entry = data_struct.SecurityInfo()
one_entry.code = row[0]
one_entry.name = row[1]
one_entry.dirpath = row[2]
r[row[0]] = one_entry
row = dbcur.fetchone()
return r
def load_daily_md(filepath, yyyymmdd, dbcur, inventory_ranges ):
with io.open( filepath, "r", encoding='utf-8') as the_file:
reader = csv.DictReader( the_file, dialect = 'excel-tab')
sec_info = db_operator.get_sec_info(dbcur)
row_num = 0
for row in reader:
row_num +=1
if 1== row_num:
verify_daily_csv_format(filepath, row )
md_record = data_struct.MdRecord()
r = md_record.load_from_daily_csv_row( filepath, row, yyyymmdd)
if not r:
continue
# 如果某证券从未由‘历史’文件倒入过，那么不会在SecurityInfo里有记录，所以
# 不能 if md_record.code not in inventory_ranges: xxxx
#
if md_record not in sec_info:
info = data_struct.SecurityInfo( )
info.code = md_record.code
info.name = md_record.name
#info.dump()
db_operator.save_sec_info_to_db_if_not_exists(dbcur, info)
db_operator.save_MD_to_db( dbcur, md_record)
dbcur.connection.commit()
print "%s was imported" % filepath