def computeTrend(records):
count = len(records)
for i in range(0,count):
if count-i>2:
t3 = comm.get_trend(records[i:i+3])
sql = 'update stock_daily set trend_3=%s where pk_id=%s' % (t3,records[i].pk_id)
dbw.query(sql)
if count-i>4:
t5 = comm.get_trend(records[i:i+5])
sql = 'update stock_daily set trend_5=%s where pk_id=%s' % (t5,records[i].pk_id)
dbw.query(sql)
def computeTrend(records):
count = len(records)
for i in range(0, count):
if count - i > 2:
t3 = comm.get_trend(records[i:i + 3])
sql = 'update stock_daily set trend_3=%s where pk_id=%s' % (
t3, records[i].pk_id)
dbw.query(sql)
if count - i > 4:
t5 = comm.get_trend(records[i:i + 5])
sql = 'update stock_daily set trend_5=%s where pk_id=%s' % (
t5, records[i].pk_id)
dbw.query(sql)
def run():
dates = da.stockdaily_cud.load_trade_dates()
today = dates[0].trade_date
date_3 = dates[2].trade_date
date_3_rows = da.stockdaily_cud.load_by_beginDate(date_3)
stocks = da.stockbaseinfos.load_all_stocks() #[web.storage(stock_no='000001',pk_id=332)] #test
for s in stocks:
rows = [r for r in date_3_rows if r.stock_no==s.stock_no]
if not rows:
continue
print s.stock_no,comm.get_trend(rows)
da.stockdaily_cud.update_trend3(today,s.stock_no,comm.get_trend(rows))
send_report(today)
def run():
dates = da.stockdaily_cud.load_trade_dates()
today = dates[0].trade_date
date_3 = dates[2].trade_date
date_3_rows = da.stockdaily_cud.load_by_beginDate(date_3)
stocks = da.stockbaseinfos.load_all_stocks(
) #[web.storage(stock_no='000001',pk_id=332)] #test
for s in stocks:
rows = [r for r in date_3_rows if r.stock_no == s.stock_no]
if not rows:
continue
print s.stock_no, comm.get_trend(rows)
da.stockdaily_cud.update_trend3(today, s.stock_no,
comm.get_trend(rows))
send_report(today)
def process1(stock_no):
records = datafile.load_raw_records(stock_no)
count = len(records)
# print 'trade_date,close,peak5,peak10'
for i in range(0,count):
stock_no_infos = stock_no.split('.')
records[i].stock_no = stock_no #stock_no_infos[0]
records[i].stock_pycode = stock_no_infos[1]
#5日内波峰波谷
records[i].peak_trough_5 = comm.get_peak_trough(records,count,i,3)
##records[i].peak_trough_10 = comm.get_peak_trough(records,count,i,5) #和5得出的结论基本吻合
#100天内，最高值high，最低值low分布, 日期&具体的值
rows = sorted(records[i:i+100], cmp=lambda x,y : cmp(x.close, y.close))
records[i].days100_low_close = rows[0].close
records[i].days100_low_date = rows[0].trade_date
records[i].days100_high_close = rows[-1].close
records[i].days100_high_date = rows[-1].trade_date
#成交量
r10 = records[i:i+10]
l = [r.volume for r in r10]
volume_avg_10 = reduce(lambda x, y: x + y, l) / len(l)
volume_p = float(records[i].volume) / volume_avg_10 if volume_avg_10 else 0
records[i].volume_avg_10 = volume_avg_10
records[i].volume_level = comm.get_volume_level(volume_p)
#移动平均线
MAs = comm.get_ma(records,i)
# print MAs
ma_5 = records[i].ma_5 = MAs['ma_5']
ma_10 = records[i].ma_10 = MAs['ma_10']
records[i].ma_20 = MAs['ma_20']
records[i].ma_50 = MAs['ma_50']
records[i].ma_100 = MAs['ma_100']
records[i].ma_200 = MAs['ma_200']
records[i].ma_p_2 = MAs['ma_p_2']
records[i].ma_p_3 = MAs['ma_p_3']
records[i].ma_p_4 = MAs['ma_p_4']
records[i].ma_p_5 = MAs['ma_p_5']
records[i].close_ma_5 = records[i].close > MAs['ma_5']
records[i].close_ma_10 = records[i].close > MAs['ma_10']
records[i].close_ma_20 = records[i].close > MAs['ma_20']
records[i].close_ma_50 = records[i].close > MAs['ma_50']
records[i].close_ma_100 = records[i].close > MAs['ma_100']
records[i].close_ma_200 = records[i].close > MAs['ma_200']
# for i in range(2,6):
# key = 'ma_p_%s' % (i)
# records[i][key] = MAs[key] #几条移动平均线的上下关系
ma_5_10 = 0
if ma_5<>0 and ma_10<>0:
ma_5_10 = 2 if ma_5>ma_10 else 1
records[i].ma_5_10 = ma_5_10
records[i].volume_cos_10 = comm.cos_dist( [r.volume for r in records[i:i+5]])
#蜡烛图形态
candles = comm.get_candle_2(records[i].open,records[i].close,records[i].high,records[i].low)
records[i].range_1 = candles[0]
records[i].range_2 = candles[1]
records[i].range_3 = candles[2]
records[i].candle_sort = candles[4]
records[i].up_or_down = 2 if candles[1]>0 else 1
records[i].high_low = records[i].high - records[i].low
records[i].close_open = records[i].close - records[i].open
records[i].jump_level = 0
if (count-i) > 1:
records[i].last_close = records[i+1].close
records[i].last_acp = records[i+1].acp
records[i].open_lastclose = records[i].open - records[i].last_close
records[i].jump_rate = records[i].open_lastclose / records[i].last_close
records[i].jump_level = comm.get_jump_level(records[i].jump_rate)
records[i].price_rate = (records[i].close - records[i].last_close) / records[i].last_close
records[i].high_rate = (records[i].high - records[i].last_close) / records[i].last_close
records[i].low_rate = (records[i].low - records[i].last_close) / records[i].last_close
records[i].hig_low_rate = records[i].high_rate - records[i].low_rate
# current_record = records[i]
# print '%s,%s,%s,%s' %(current_record.trade_date,current_record.close,records[i].peak_trough_5,records[i].peak_trough_10)
records[i].trend_3 = comm.get_trend(records[i:i+3]) if count-i>2 else 0
records[i].trend_5 = comm.get_trend(records[i:i+5]) if count-i>4 else 0
records[i].future1_prate = 0
records[i].future1_range = 0
if i>1:
prate = (records[i-2].close - records[i-1].close) / records[i-1].close
frange = comm.getFutureRange(prate)
records[i].future1_prate = prate
records[i].future1_range = frange
records[i].future2_prate = 0
records[i].future2_range = 0
if i>2:
prate = (records[i-3].close - records[i-1].close) / records[i-1].close
frange = comm.getFutureRange(prate)
records[i].future2_prate = prate
records[i].future2_range = frange
records[i].future3_prate = 0
records[i].future3_range = 0
if i>3:
prate = (records[i-4].close - records[i-1].close) / records[i-1].close
frange = comm.getFutureRange(prate)
records[i].future3_prate = prate
records[i].future3_range = frange
#print records[i]
# comm.get_test(records,i)
# datafile.gen_date_file(records[i])
datafile.save_stocks(stock_no,records)
return records
def process1(stock_no):
records = datafile.load_raw_records(stock_no)
count = len(records)
# print 'trade_date,close,peak5,peak10'
for i in range(0, count):
stock_no_infos = stock_no.split('.')
records[i].stock_no = stock_no #stock_no_infos[0]
records[i].stock_pycode = stock_no_infos[1]
#5日内波峰波谷
records[i].peak_trough_5 = comm.get_peak_trough(records, count, i, 3)
##records[i].peak_trough_10 = comm.get_peak_trough(records,count,i,5) #和5得出的结论基本吻合
#100天内，最高值high，最低值low分布, 日期&具体的值
rows = sorted(records[i:i + 100],
cmp=lambda x, y: cmp(x.close, y.close))
records[i].days100_low_close = rows[0].close
records[i].days100_low_date = rows[0].trade_date
records[i].days100_high_close = rows[-1].close
records[i].days100_high_date = rows[-1].trade_date
#成交量
r10 = records[i:i + 10]
l = [r.volume for r in r10]
volume_avg_10 = reduce(lambda x, y: x + y, l) / len(l)
volume_p = float(
records[i].volume) / volume_avg_10 if volume_avg_10 else 0
records[i].volume_avg_10 = volume_avg_10
records[i].volume_level = comm.get_volume_level(volume_p)
#移动平均线
MAs = comm.get_ma(records, i)
# print MAs
ma_5 = records[i].ma_5 = MAs['ma_5']
ma_10 = records[i].ma_10 = MAs['ma_10']
records[i].ma_20 = MAs['ma_20']
records[i].ma_50 = MAs['ma_50']
records[i].ma_100 = MAs['ma_100']
records[i].ma_200 = MAs['ma_200']
records[i].ma_p_2 = MAs['ma_p_2']
records[i].ma_p_3 = MAs['ma_p_3']
records[i].ma_p_4 = MAs['ma_p_4']
records[i].ma_p_5 = MAs['ma_p_5']
records[i].close_ma_5 = records[i].close > MAs['ma_5']
records[i].close_ma_10 = records[i].close > MAs['ma_10']
records[i].close_ma_20 = records[i].close > MAs['ma_20']
records[i].close_ma_50 = records[i].close > MAs['ma_50']
records[i].close_ma_100 = records[i].close > MAs['ma_100']
records[i].close_ma_200 = records[i].close > MAs['ma_200']
# for i in range(2,6):
# key = 'ma_p_%s' % (i)
# records[i][key] = MAs[key] #几条移动平均线的上下关系
ma_5_10 = 0
if ma_5 <> 0 and ma_10 <> 0:
ma_5_10 = 2 if ma_5 > ma_10 else 1
records[i].ma_5_10 = ma_5_10
records[i].volume_cos_10 = comm.cos_dist(
[r.volume for r in records[i:i + 5]])
#蜡烛图形态
candles = comm.get_candle_2(records[i].open, records[i].close,
records[i].high, records[i].low)
records[i].range_1 = candles[0]
records[i].range_2 = candles[1]
records[i].range_3 = candles[2]
records[i].candle_sort = candles[4]
records[i].up_or_down = 2 if candles[1] > 0 else 1
records[i].high_low = records[i].high - records[i].low
records[i].close_open = records[i].close - records[i].open
records[i].jump_level = 0
if (count - i) > 1:
records[i].last_close = records[i + 1].close
records[i].last_acp = records[i + 1].acp
records[i].open_lastclose = records[i].open - records[i].last_close
records[i].jump_rate = records[i].open_lastclose / records[
i].last_close
records[i].jump_level = comm.get_jump_level(records[i].jump_rate)
records[i].price_rate = (records[i].close - records[i].last_close
) / records[i].last_close
records[i].high_rate = (records[i].high - records[i].last_close
) / records[i].last_close
records[i].low_rate = (
records[i].low - records[i].last_close) / records[i].last_close
records[
i].hig_low_rate = records[i].high_rate - records[i].low_rate
# current_record = records[i]
# print '%s,%s,%s,%s' %(current_record.trade_date,current_record.close,records[i].peak_trough_5,records[i].peak_trough_10)
records[i].trend_3 = comm.get_trend(records[i:i +
3]) if count - i > 2 else 0
records[i].trend_5 = comm.get_trend(records[i:i +
5]) if count - i > 4 else 0
records[i].future1_prate = 0
records[i].future1_range = 0
if i > 1:
prate = (records[i - 2].close -
records[i - 1].close) / records[i - 1].close
frange = comm.getFutureRange(prate)
records[i].future1_prate = prate
records[i].future1_range = frange
records[i].future2_prate = 0
records[i].future2_range = 0
if i > 2:
prate = (records[i - 3].close -
records[i - 1].close) / records[i - 1].close
frange = comm.getFutureRange(prate)
records[i].future2_prate = prate
records[i].future2_range = frange
records[i].future3_prate = 0
records[i].future3_range = 0
if i > 3:
prate = (records[i - 4].close -
records[i - 1].close) / records[i - 1].close
frange = comm.getFutureRange(prate)
records[i].future3_prate = prate
records[i].future3_range = frange
#print records[i]
# comm.get_test(records,i)
# datafile.gen_date_file(records[i])
datafile.save_stocks(stock_no, records)
return records