def profit():
overall_history_amount = 0.0
overall_history_profit = 0.0
overall_current_amount = 0.0
overall_current_profit = 0.0
for stock in stockdata.all_stocks_1:
if stock.has_key('trades'):
code = stock['code']
#print 'Getting realtime quotes...'
df = ts.get_realtime_quotes(code)
today_price = float(df['price'][0])
print code + ' ' + df['name'][0]
history_amount = 0.0
history_profit = 0.0
current_amount = 0.0
current_profit = 0.0
for trade in stock['trades']:
theDate = dateutil2.parse_date(trade[0])
direction = trade[1]
volume = trade[2]
price = trade[3]
if direction == 2 or len(trade) > 4:
theSellDate = dateutil2.parse_date(trade[4])
sellPrice = trade[5]
history_amount += price * volume
history_profit += (sellPrice - price) * volume
if direction == 1:
current_amount += price * volume
current_profit += (today_price - price) * volume
if history_amount != 0.0:
print '历史: %s/%s \t%5.2f%%' % (
str(history_amount), str(history_profit),
history_profit / history_amount * 100)
if current_amount != 0.0:
print '持仓: %s/%s \t%5.2f%%' % (
str(current_amount), str(current_profit),
current_profit / current_amount * 100)
overall_history_amount += history_amount
overall_history_profit += history_profit
overall_current_amount += current_amount
overall_current_profit += current_profit
print ''
print '\n\n'
if overall_history_amount != 0.0:
print '历史: %s/%s \t%5.2f%%' % (
str(overall_history_amount), str(overall_history_profit),
overall_history_profit / overall_history_amount * 100)
if overall_current_amount != 0.0:
print '持仓: %s/%s \t%5.2f%%' % (
str(overall_current_amount), str(overall_current_profit),
overall_current_profit / overall_current_amount * 100)
def previous_data_with_date(code, datestr, log, recursive=False):
# mem cache
codedate = code + '__' + datestr
if dayK_cache.has_key(codedate):
return dayK_cache[codedate]
originDateStr = datestr
dh = dbman.query_dayK(code, datestr)
if len(dh) == 0:
log('Getting hist data for %s at %s' % (code, datestr))
df = ts.get_hist_data(code, start=datestr, end=datestr)
print 'x'
log('Done hist data for %s at %s' % (code, datestr))
d = dateutil2.parse_date(datestr)
if df is None or df.empty:
if not recursive:
return None
d = dateutil2.previous_date(d)
datestr = dateutil2.format_date(d)
#df = ts.get_hist_data(code, start=datestr, end=datestr)
log('Nest previous_data_with_date call %s %s' % (code, datestr))
dh = previous_data_with_date(code, datestr, log)
dbman.insert_dayK(code, False, originDateStr, dh['open'],
dh['close'], dh['high'], dh['low'])
else:
dbman.insert_dayK(code, True, originDateStr, df['open'][0],
df['close'][0], df['high'][0], df['low'][0])
dh = dbman.query_dayK(code, originDateStr)
dayK_cache[codedate] = dh[0]
return dh[0]
def last_trade(code):
print 'Getting realtime quotes...'
df = ts.get_realtime_quotes(code)
today_price = float(df['price'][0])
print code + ' ' + df['name'][0]
for stock in stockdata.all_stocks_1:
if stock['code'] == code and stock.has_key('trades'):
last_buy_date = date.min
last_buy = 0.0
last_buy_position = 0
for trade in stock['trades']:
theDate = dateutil2.parse_date(trade[0])
direction = trade[1]
volume = trade[2]
price = trade[3]
if direction == 1 and theDate >= last_buy_date:
last_buy_date = theDate
last_buy = price
last_buy_position = volume
if last_buy != 0.0:
print 'Last trade: ' + str(last_buy_date) + ' ' + str(
last_buy) + ' ' + str(last_buy_position)
print '当前价: ' + str(today_price)
print '盈利: ' + '%3.2f%%' % (
(today_price - last_buy) / last_buy * 100)
high = get_high(code, last_buy_date)
print ''
print '最高价: ' + str(high)
print '回撤: ' + '%2.0f%%' % ((high - today_price) /
(high - last_buy) * 100)
def compute_KDJ933(stock, theDate, close, high, low, real, log):
baseKDJDateStr = stock['KDJ'].keys()[0]
if dateutil2.parse_date(baseKDJDateStr) == theDate:
#print 'hit'
return (stock['KDJ'][baseKDJDateStr][0],
stock['KDJ'][baseKDJDateStr][1],
stock['KDJ'][baseKDJDateStr][2])
(k_1, d_1, j_1) = get_previous_KDJ933(stock,
dateutil2.previous_date(theDate),
log)
if not real:
return (k_1, d_1, j_1)
#print 'compute' + stock['code'] + ' ' + theDate.strftime('%Y-%m-%d') + ' ' + str(k_1) + ' ' + str(d_1) + " " + str(j_1)
h9 = high
l9 = low
count = 1
while count < 9:
theDate = dateutil2.previous_date(theDate)
datestr = dateutil2.format_date(theDate)
dh = tdal.previous_data_with_date(stock['code'], datestr, log)
if dh['real']:
count += 1
if l9 > dh['low']:
l9 = dh['low']
if h9 < dh['high']:
h9 = dh['high']
rsv = (close - l9) / (h9 - l9) * 100
#print "rsv " + str(rsv) + " l9 " + " " + str(l9) + " h9 " + str(h9)
k = rsv / 3 + 2 * k_1 / 3
d = k / 3 + 2 * d_1 / 3
j = 3 * k - 2 * d
return (k, d, j)
def advise(stock, total, index):
df = all_stocks_realtime_quotes
code = stock['code']
current_price = float(df['price'][index])
today_high = float(df['high'][index])
today_low = float(df['low'][index])
today_open = float(df['open'][index])
position = stock['position']
last_sell = stock['last_sell']
last_buy = stock['last_buy']
# name
stock['name'] = df['name'][index]
namelen = strutil.width(stock['name'])
if namelen < 8:
for i in range(8 - namelen):
stock['name'] += ' ' # '_'
#print len(stock['name'])
#print namelen
dh = tdal.previous_data(stock['code'], log_status)
previous_close = float(dh['close'])
previous_open = float(dh['open'])
action = ''
action_color = 1
stock['action_color'] = action_color
recent_low = get_recent_low(stock)
recent_rise_rate = (current_price - recent_low
) / recent_low * 100 if recent_low != 0 else 0.0
currentAmount = 0.0
if stock.has_key(keys.trades):
for trade in stock[keys.trades]:
direction = trade[1]
volume = trade[2]
if direction == 1:
currentAmount += volume * current_price
stock['currentAmount'] = currentAmount
last_profit = stock['last_buy_position'] * (current_price - last_buy)
if position == 0 and recent_rise_rate >= 20.0 and recent_rise_rate <= 28.0 and not stock.has_key(
'margin'):
pass
# 设置了margin
elif not g_show_all and stock.has_key('margin'):
if len(stock['margin']) > 1 and stock['margin'][0] < current_price and stock['margin'][1] > current_price \
or len(stock["margin"]) == 1 and stock['margin'][0] < current_price and (position == 0 or current_price < stock["last_buy"] * (1 + const.const_profitPercent)):
stock['action'] = "HIDE"
return
elif last_profit > 0 and last_profit < 110.0:
stock['action'] = "HIDE"
return
# 设置了reminder
elif not g_show_all and stock.has_key('reminder'):
if date.today() > dateutil2.parse_date(stock['reminder']):
stock['comment'] = 'REMINDER'
#stock['action'] = "买入"
else:
stock['action'] = "HIDE"
return
# last_buy * 0.x < current < last_buy * 1.x (暗示有仓位)
elif not g_show_all and current_price > stock["last_buy"] * (
1 - const.const_deficitPercent
) and current_price < stock["last_buy"] * (1 + const.const_profitPercent):
stock['action'] = "HIDE"
return
# 空仓，但操作过，卖飞了
elif not g_show_all and position == 0 and stock[
"last_sell"] > 0 and current_price * (
1 + const.const_profitPercent) > stock["last_sell"]:
stock['action'] = "HIDE"
return
# 空仓，但操作过，没跌到位。熊市启用，震荡市可comment掉
elif not g_show_all and position == 0 and stock[
"last_sell"] > 0 and current_price > stock["last_sell"] * (
1 - const.const_deficitPercent):
stock['action'] = "HIDE"
return
# profit不足110
elif not g_show_all and last_profit > 0 and last_profit < 110.0:
stock['action'] = "HIDE"
return
j = 0
#if stock.has_key('KDJ'):
# (k, d, j) = kdj.get_today_KDJ933(stock, current_price, today_high, today_low, log_status)
# TODO: fix kdj
if today_open == 0:
action = " "
elif position == 0 and recent_rise_rate >= 20.0 and recent_rise_rate <= 28.0:
action = '买入'
action_color = 2
elif current_price - today_open > 0:
if previous_close - previous_open < 0:
strong_buy = whether_strong_buy(current_price, last_sell, last_buy)
strong_buy = False if (j > 80) else strong_buy
if stock.has_key('last_buy_date'):
# 距离上次买入需超过3个月或下跌超过30%
strong_buy = False if (date.today() - datetime.strptime(stock['last_buy_date'], '%Y-%m-%d').date()).days < 90 or \
current_price > stock["last_buy"] * 0.70 else strong_buy
if strong_buy:
action = "买入"
action_color = 2
else:
action = "弱买"
action_color = 4
else:
if stock['position'] == 0:
action = "追高"
action_color = 4
else:
action = "持有"
elif current_price - today_open < 0:
if previous_close - previous_open < 0 or position == 0:
action = "观望"
elif current_price < last_buy:
action = "亏卖"
action_color = 5
elif current_price > last_buy + 1.00 and current_price < last_buy * 1.1:
strong_sell = whether_strong_sell(stock, current_price, last_sell,
last_buy, today_high)
if strong_sell:
action = "卖出"
action_color = 3
else:
action = "弱卖"
action_color = 5
elif current_price < last_buy + 1.00 or current_price < last_buy * 1.1:
action = "薄卖"
action_color = 5
else:
strong_sell = whether_strong_sell(stock, current_price, last_sell,
last_buy, today_high)
if strong_sell:
action = "卖出"
action_color = 3
else:
action = "忖卖"
action_color = 5
elif current_price == 0:
action = " "
else:
action = " -- "
stock['action'] = action
stock['action_color'] = action_color
profit_percent = 0
if last_buy > 0: #and position > 0:
profit_percent = math.floor(
(current_price - last_buy) / last_buy * 100)
elif last_sell > 0: #and position > 0:
profit_percent = math.floor(
(current_price - last_sell) / last_sell * 100)
if profit_percent > 0:
profit_percent = 0
#if profit_percent <= -10:
# profit_percent = 0
if profit_percent == 0:
profit_percentstr = ' '
else:
profit_percentstr = '%3d%%' % profit_percent
regress_rate = 0
if profit_percent <= 0:
regress_ratestr = ' '
else:
recent_high = get_recent_high(stock, today_high)
regress_rate = math.ceil(
(recent_high - current_price) / (recent_high - last_buy) * 100)
#if code == '002299':
# print str(recent_high) + ' ' + str(current_price) + ' ' + str(last_buy)
regress_ratestr = '%2d%%' % regress_rate
if position == 0 and recent_rise_rate > 0:
regress_rate = recent_rise_rate
regress_ratestr = '%2d%%' % regress_rate
if (current_price == 0 or today_open == 0) and (code
not in posman.halt_codes):
posman.halt_codes.append(code)
pp.preprocess_all(all_stocks, stockdata.sh_index, log_status)
(last, far) = util.get_hold_duration(stock)
durationstr = ' '
if position > 0:
durationstr = '%2d/%2d' % (last, far)
stack = 0
stackstr = ' '
if stock.has_key('trades'):
for trade in stock['trades']:
direction = trade[1]
if direction == 1:
stack += 1
if stack <= 5:
stackstr = stackstr.replace(' ', '|', 1)
elif stack <= 10:
stackstr = stackstr.replace('|', '+', 1)
elif stack <= 15:
stackstr = stackstr.replace('+', '#', 1)
else:
stackstr = stackstr.replace('#', '$', 1)
index_profit_percent = 0
buy_index = 0
if position > 0:
index_dh = tdal.previous_data_with_date(stockdata.sh_index['code'],
stock['last_buy_date'],
log_status)
buy_index = (index_dh['high'] + index_dh['low']) / 2
index_profit_percent = (stockdata.sh_index['price'] -
buy_index) / buy_index
index_profit_percentstr = ' '
if index_profit_percent != 0:
index_profit_percentstr = '%6.2f%%' % (index_profit_percent * 100)
index_cost_percent = 0
#TODO: index_cost
#if stock.has_key('last_sell_date'):
# index_dh = tdal.previous_data_with_date(stockdata.sh_index['code'], stock['last_sell_date'], log_status)
# sell_index = (index_dh['high'] + index_dh['low']) / 2
# # 处理累进买入的情况
# if buy_index != 0 and sell_index > buy_index and stock['last_sell_date'] != stock['last_buy_date']:
# sell_index = buy_index
# index_cost_percent = (stockdata.sh_index['price'] - sell_index) / sell_index
index_cost_percentstr = ' '
#if index_cost_percent < 0:
# index_cost_percentstr = '%6.2f%%' % (index_cost_percent * 100)
stock['more_info_previousChange'] = previous_close - previous_open
stock['more_info_todayChange'] = current_price - today_open
stock['more_info_currentPrice'] = current_price
stock['more_info_lastBuy'] = stock['last_buy']
stock['more_info_lastSell'] = stock['last_sell']
stock['more_info_position'] = stock['last_buy_position'] #position
stock['more_info_profit_percent'] = profit_percent
stock['more_info_profit_percentstr'] = profit_percentstr
stock['more_info_regress_rate'] = regress_rate
stock['more_info_regress_ratestr'] = regress_ratestr
stock['more_info_currentJ'] = j
stock['more_info_duration_last'] = last
stock['more_info_duration_far'] = far
stock['more_info_durationstr'] = durationstr
stock['more_info_stack'] = stack
stock['more_info_stackstr'] = stackstr
stock['more_info_index_profit_percent'] = index_profit_percent
stock['more_info_index_profit_percentstr'] = index_profit_percentstr
stock['more_info_index_cost_percent'] = index_cost_percent
stock['more_info_index_cost_percentstr'] = index_cost_percentstr
stock['more_info_today_change'] = position * (current_price -
previous_close)
def preprocess_stock(stock, sh_index, log):
last_buy = 0.0
last_buy_date = date.min
far_buy_date = date.max
last_sell = 0.0
last_sell_date = date.min
position = 0
amount = 0
last_buy_position = 0
turnover = 0
if stock.has_key(keys.trades):
for trade in stock[keys.trades]:
theDate = dateutil2.parse_date(trade[0])
direction = trade[1]
volume = trade[2]
price = trade[3]
if direction == 2 or len(trade) > 4:
theSellDate = dateutil2.parse_date(trade[4])
sellPrice = trade[5]
# correct direction in case it has wrong value
if direction != 2:
print 'wrong direction in stock ' + stock['code']
direction = 2
if theSellDate >= last_sell_date:
last_sell_date = theSellDate
last_sell = sellPrice
turnover += 1
if direction == 1 and theDate >= last_buy_date:
last_buy_date = theDate
last_buy = price
last_buy_position = volume
if direction == 1 and theDate < far_buy_date:
far_buy_date = theDate
if direction == 1:
position += direction * volume
amount += volume * price
posman.investments['total'] += direction * volume * price
if stock['code'] not in posman.whitelist_codes:
posman.investments['totalExceptWhitelist'] += direction * volume * price
if (stock['code'] not in posman.whitelist_codes) and (stock['code'] not in posman.halt_codes):
posman.investments['totalExceptWhitelistAndHalt'] += direction * volume * price
if stock['code'] in posman.vip_codes:
posman.investments['totalVip'] += direction * volume * price
#TODO: cost_index
'''
# sh index at trade date
# print '#### getting sh_index at date ' + trade[0]
dh = tdal.previous_data_with_date(sh_index['code'], trade[0], log)
# print '#### end sh_index at date ' + trade[0]
#dh = tdal.previous_data_with_date(sh_index['code'], trade[0], log)
shIndex = (dh['high'] + dh['low']) / 2
posman.investments['indexedTotal'] += shIndex * volume * price
costIndex = int(math.floor((5000 - shIndex) / 500) + 1)
costIndex = 0 if (costIndex < 0) else costIndex
costIndex = 7 if (costIndex > 7) else costIndex
posman.investments['indexed_cost'][costIndex] += volume * price
if sh_index['price'] > 0:
costIndex = (int(sh_index['price']) / 100) - (int(shIndex) / 100) + 3
#display_info("" + costIndex + " " + amount * price, 1, 20)
if costIndex >= 0 and costIndex <= 7:
posman.investments['fine_indexed_cost'][costIndex] += volume * price
'''
if not stock.has_key('last_buy_date') and last_buy_date != date.min:
stock['last_buy_date'] = last_buy_date.strftime('%Y-%m-%d')
if not stock.has_key('far_buy_date') and far_buy_date != date.max:
stock['far_buy_date'] = far_buy_date.strftime('%Y-%m-%d')
if not stock.has_key('last_buy'):
stock['last_buy'] = last_buy
if not stock.has_key('last_buy_position'):
stock['last_buy_position'] = last_buy_position
if not stock.has_key('last_sell_date') and last_sell_date != date.min:
stock['last_sell_date'] = last_sell_date.strftime('%Y-%m-%d')
if not stock.has_key('last_sell'):
stock['last_sell'] = last_sell
if not stock.has_key('position'):
stock['position'] = position
if stock['position'] > 0:
posman.investments['positioned_stock_count'] += 1
stock['turnover'] = turnover
stock['amount'] = amount