def bigFallOpen(self, stocks):
# 大幅低开，放心追
rise_threshold = -0.07
buy_stocks = []
for stock in stocks:
opens = history_bars(stock, self.period, '1d', 'open')
close = history_bars(stock, self.period, '1d', 'close')
# increase_rate = (now - past) / past
open_rate = (opens[-1] - close[-2]) / close[-2]
if open_rate < rise_threshold:
buy_stocks.append(stock)
return buy_stocks
def search(self, stocks, sc_dict, today):
sell_stocks = []
for stock in stocks:
buy_date = sc_dict[stock][0]
period = (today - buy_date).days + 1
highs = history_bars(stock, period, '1d', 'high')
close = history_bars(stock, period, '1d', 'close')
# increase_rate = (now - past) / past
retreat_rate = (close[-1] - highs.max()) / highs.max()
if retreat_rate <= self.retreat_threshold:
# pdb.set_trace()
sell_stocks.append(stock)
return sell_stocks
def riseStopCheck(self, stocks):
# 选出日涨停，且不是连续涨停
check_period = 10
selected_stocks = []
for stock in stocks:
high_prices = history_bars(stock, check_period+1, '1d', 'high')
close_prices = history_bars(stock, check_period+1, '1d', 'close')
# 要求收盘价等于最高价，但要求收盘价高于9.95%
if (Utility.isRiseStopNow(close_prices) and close_prices[-1] == high_prices[-1]
and not Utility.isContinueRiseStop(close_prices, max_co_present=2)):
selected_stocks.append(stock)
self.logger.debug('选出当日涨停，且不连续涨停的股票%s' % stock)
return selected_stocks
def breakStrength(self, stocks):
# 收盘价创3个交易日新高， 且选出日未出现跳空缺口
check_period = 3
selected_stocks = []
for stock in stocks:
close_prices = history_bars(stock, check_period, '1d', 'close')
open_prices = history_bars(stock, check_period, '1d', 'open')
high_prices = history_bars(stock, check_period, '1d', 'high')
low_prices = history_bars(stock, check_period, '1d', 'low')
if close_prices.max() == close_prices[-1]:
if open_prices[-1] <= high_prices[-2] and open_prices[-1] >= low_prices[-2]:
selected_stocks.append(stock)
self.logger.debug('选出收盘价创新高且未出现跳空缺口的股票%s' % stock)
return selected_stocks
def aboveBoolMiddelLine(self, stocks):
# 高于布林线中轨
# 且必须是红盘才考虑买股票
buy_stocks = []
for stock in stocks:
close = history_bars(stock, self.period, '1d', 'close')
opens = history_bars(stock, self.period, '1d', 'open')
upper, middle, lower = talib.BBANDS(close,
timeperiod=20,
nbdevup=2,
nbdevdn=2,
matype=0)
if close[-1] >= middle[-1] and close[-1] > opens[-1]:
buy_stocks.append(stock)
return buy_stocks
def bigRiseOpen(self, stocks):
# 这条规则应该优化成，涨停开盘，如果开板就追一把。
rise_threshold = 0.07
rise_stop = 0.095
buy_stocks = []
for stock in stocks:
opens = history_bars(stock, self.period, '1d', 'open')
close = history_bars(stock, self.period, '1d', 'close')
# increase_rate = (now - past) / past
open_rate = (opens[-1] - close[-2]) / close[-2]
if open_rate > rise_stop:
# 开盘涨停的就不用分析了，跟不上的
continue
if open_rate > rise_threshold:
buy_stocks.append(stock)
return buy_stocks
def search(self, stocks):
uc_97 = 0
uc_85 = 0
uc_50 = 0
uc_15 = 0
uc_3 = 0
total = len(stocks)
for stock in stocks:
close_prices = history_bars(stock, self.period, '1d', 'close')
if len(close_prices) < 90:
# print('股票：%s, 交易日：%d' % (stock,len(close_prices)))
total -= 1
continue
if self.LessThantQ(close_prices, 97):
uc_97 += 1
if self.LessThantQ(close_prices, 85):
uc_85 += 1
if self.LessThantQ(close_prices, 50):
uc_50 += 1
if self.LessThantQ(close_prices, 15):
uc_15 += 1
if self.LessThantQ(close_prices, 3):
uc_3 += 1
# upper_rate = upper_counter / total
# lower_rate = lower_counter / total
# 如果 uc_50/total 大于 0.5, 说明两市超过50%的股票价格处于低位，熊市特征。
# 如果 uc_3/total 大于 0.5, 说明两市超过50%的股票价格处于极低水平，股市超跌。
if uc_3 / total > 0.5:
return 1
return 0
def stepBack(self, stocks):
# 回踩5/10日线企稳，意思就是前一天回踩5日线，然后今天依然在5日线之上
buy_stocks = []
for stock in stocks:
lows = history_bars(stock, self.period, '1d', 'low')
close = history_bars(stock, self.period, '1d', 'close')
avg5 = talib.MA(close, timeperiod=5, matype=0)
avg10 = talib.MA(close, timeperiod=10, matype=0)
if lows[-2] < avg5[-2] and close[-2] > avg5[-2] and close[
-1] > avg5[-1]:
# 前一天回踩5日线，今天依然收盘在5日线之上
buy_stocks.append(stock)
elif lows[-2] < avg10[-2] and close[-2] > avg10[-2] and close[
-1] > avg10[-1]:
# 前一天回踩10日线，今天依然收盘在10日线之上
buy_stocks.append(stock)
return buy_stocks
def calTrend(self, market_index):
# 计算大盘多空趋势的核心算法
ema_period = 28
close_prices = history_bars(market_index, self.period, '1d', 'close')
close_ema = talib.EMA(close_prices, timeperiod=ema_period)
today = close_ema[-1]
yesterday = close_ema[-2]
return today, yesterday
def smallRiseOpen(self, stocks):
# 小幅高开，看看再追
rise_threshold = 0.05
buy_stocks = []
for stock in stocks:
opens = history_bars(stock, self.period, '1d', 'open')
close = history_bars(stock, self.period, '1d', 'close')
lows = history_bars(stock, self.period, '1d', 'low')
# increase_rate = (now - past) / past
open_rate = (opens[-1] - close[-2]) / close[-2]
if open_rate < rise_threshold and open_rate > 0:
if lows[-1] <= close[-2] \
and close[-1] > opens[-1]:
# 回补日内缺口
# 且必须收红
buy_stocks.append(stock)
return buy_stocks
def volShrinkCheck(self, stocks):
check_period = 3
selected_stocks = []
for stock in stocks:
close_prices = history_bars(stock, check_period+1, '1d', 'volume')
if Utility.isabsoluteDesc(close_prices[:-1]):
selected_stocks.append(stock)
self.logger.debug('%s股票选出日前连续3天缩量下跌' % stock)
return selected_stocks
def breakAvgsAdv(self, stocks):
# 突破中期均线, 这个规则必须放在最后
selected_stocks = []
for stock in stocks:
# 突破标志位
open_prices = history_bars(stock, self.load_period, '1d', 'open')
close_prices = history_bars(stock, self.load_period, '1d', 'close')
avg20 = talib.MA(close_prices, timeperiod=20, matype=0)
avg30 = talib.MA(close_prices, timeperiod=30, matype=0)
avg60 = talib.MA(close_prices, timeperiod=60, matype=0)
# 如果确实突破了中期均线，且没有上方压力，那么就可以选择这个股票
# 计算上穿的中期均线的数量和名称
break_through = self._avgCrossFind(open_prices[-1], close_prices[-1], avg20[-1], avg30[-1], avg60[-1])
if self._strengthCheck(break_through, open_prices[-1], close_prices[-1], avg20[-1], avg30[-1], avg60[-1]):
selected_stocks.append(stock)
# 将突破结果写入 暂存字典
self.select_info[stock] = (close_prices[-1], '突破%s均线' % break_through)
self.logger.debug('选出突破中期均线的股票%s' % stock)
return selected_stocks
def search(self, stocks, sc_dict):
sell_stocks = []
for stock in stocks:
buy_price = sc_dict[stock][1]
close = history_bars(stock, self.period, '1d', 'close')
# increase_rate = (now - past) / past
retreat_rate = (close[-1] - buy_price) / buy_price
if retreat_rate <= self.retreat_threshold:
sell_stocks.append(stock)
return sell_stocks
def redAtTail(self, stocks):
# 尾盘红盘
buy_stocks = []
for stock in stocks:
close = history_bars(stock, self.period, '1d', 'close')
# 可以考虑用小时来判断是否是尾盘，比如2:30之后。
if close[-1] > close[-2]:
# 尾盘红盘后可考虑买入
buy_stocks.append(stock)
return buy_stocks
def tunoverRateCheck(self, stocks):
# 换手率数据取不到，因此采用成交量数据代替
# 换手率=当日成交额/流通股本
check_period = 3
selected_stocks = []
for stock in stocks:
vols = history_bars(stock, check_period, '1d', 'volume')
if Utility.isabsoluteIncr(vols):
selected_stocks.append(stock)
self.logger.debug('选出成交量逐渐放大的股票%s' % (stock))
return selected_stocks
def bollCheckAdv(self, stocks):
check_period = 100
selected_stocks = []
# 收盘价跌破下轨
for stock in stocks:
close_prices = history_bars(stock, check_period, '1d', 'close')
upper, middle, lower = talib.BBANDS(close_prices, timeperiod=20, nbdevup=2, nbdevdn=2, matype=0)
if close_prices[-1] < lower[-1]:
selected_stocks.append(stock)
self.logger.debug('%s收盘跌破Boll线下轨' % stock)
return selected_stocks
def aboveBreakAvgLine(self, stocks):
# 必须高于之前选出时，突破的那条均线
buy_stocks = []
for stock in stocks:
# 找到选出当天的信息
_, break_through = self.sdb.getSelectInfo(stock, '追涨')
close = history_bars(stock, break_through, '1d', 'close')
avg = talib.MA(close, timeperiod=break_through, matype=0)
if close[-1] > avg[-1]:
buy_stocks.append(stock)
return buy_stocks
def riseTooMuch(self, stocks):
buy_stocks = []
for stock in stocks:
# 找到涨停当天的价格
select_price, break_through = self.sdb.getSelectInfo(stock, '追涨')
# 只取五天内的收盘最高价
close = history_bars(stock, 5, '1d', 'close')
rise_rate = (max(close) - select_price) / select_price
if rise_rate <= 0.05:
buy_stocks.append(stock)
return buy_stocks
def riseRateCheck(self, stocks):
# 过去10个交易日内，涨幅小于25%
check_period = 10
selected_stocks = []
for stock in stocks:
close_prices = history_bars(stock, check_period, '1d', 'close')
rise_rate = (close_prices[-1] - close_prices[0]) / close_prices[0]
if rise_rate < 0.25:
selected_stocks.append(stock)
self.logger.debug('选出过去10个交易日内涨幅小于25%%的股票，当前涨幅%d' % (rise_rate*100))
return selected_stocks
def riseStopCheck(self, stocks):
# 连续3个涨停板，三个涨停必须满足： 1，连续； 2，只有3个，不能多。
check_period = 10
selected_stocks = []
for stock in stocks:
close_prices = history_bars(stock, check_period+1, '1d', 'close')
if (Utility.isContinueRiseStop(close_prices, max_co_present=3)
and not Utility.isContinueRiseStop(close_prices, max_co_present=4)):
selected_stocks.append(stock)
self.logger.debug('选出三连板的股票%s' % (stock))
return selected_stocks
def avg120TrendUpAdv(self, stocks):
# 250日均线，趋势向上
selected_stocks = []
for stock in stocks:
close_prices = history_bars(stock, self.load_period+3, '1d', 'close')
avg250 = talib.MA(close_prices, timeperiod=250, matype=0)
# 选出日前3天120日均线趋势向上
if avg250[-1] > avg250[-2] and avg250[-2] > avg250[-3]:
selected_stocks.append(stock)
self.logger.debug('选出120日均线趋势向上的股票%s' % stock)
return selected_stocks
def rsiCheckAdv(self, stocks):
# RSI指标低于20
rsi_threshold = 20
selected_stocks = []
for stock in stocks:
close_prices = history_bars(stock, self.load_period, '1d', 'close')
rsi_data = talib.RSI(close_prices, timeperiod=self.rsi_period)[-1]
if rsi_data < rsi_threshold:
selected_stocks.append(stock)
self.logger.debug('%s股票RSI值低于下限20，当前RSI=%d' % (stock, rsi_data))
return selected_stocks
def rsiCheck(self, stocks):
# 涨停前10个交易日内，RSI指标曾处于50以下
check_period = 10
selected_stocks = []
for stock in stocks:
close_prices = history_bars(stock, self.load_period, '1d', 'close')
rsi_data = talib.RSI(close_prices, timeperiod=self.rsi_period)[-check_period:]
if rsi_data.min() < 50:
selected_stocks.append(stock)
self.logger.debug('涨停前10个交易日内，RSI指标小于50的股票%s，目前RSI=%d' % (stock, rsi_data[-1]))
return selected_stocks
def periodRecordHigh(self, stocks):
# 创阶段性历史新高
selected_stocks = []
observation_period = 120
for stock in stocks:
close_prices = history_bars(stock, observation_period, '1d', 'close')
# 涨停且当前价格是近半年来的最高值，直接选出。
if close_prices[-1] == max(close_prices):
selected_stocks.append(stock)
# select_info = (price, reason)
self.select_info[stock] = (close_prices[-1], '创半年新高')
self.logger.debug('选出半年之内创新高的股票%s' % stock)
return selected_stocks
def seekBoardCheck(self, sc_dict):
# 涨停卖出规则：买入条件为打板时，只要不能连续涨停就卖出
sell_stocks = []
for stock in sc_dict.keys():
close = history_bars(stock, self.period, '1d', 'close')
buy_reason = sc_dict[stock][2]
if buy_reason == '打板' \
and not Utility.isRiseStopNow(close):
sell_stocks.append(stock)
# 从可卖股票中删除打板股票，以免对后续处理造成影响
for stock in sell_stocks:
sc_dict.pop(stock)
return sell_stocks
def fallStopCheck(self, stocks):
# 一月内不能出现连续跌停，且选出当日不能跌停
check_period = 20
selected_stocks = []
for stock in stocks:
close_prices = history_bars(stock, check_period+1, '1d', 'close')
if not (Utility.isContinueFallStop(close_prices, min_co_present=2)
or
Utility.isFallStopNow(close_prices)):
selected_stocks.append(stock)
else:
self.logger.debug('%s股票一月内存在连续跌停，剔除' % stock)
return selected_stocks
def breakAtMiddle(self, stocks):
# 盘中突破
rise_threshold = 0.04
buy_stocks = []
for stock in stocks:
# opens = history_bars(stock, self.period, '1d', 'open')
close = history_bars(stock, self.period, '1d', 'close')
# 涨幅 = (now - past) / past
incr_rate = (close[-1] - close[-2]) / close[-2]
if incr_rate > rise_threshold:
# 盘中涨幅超过4%
buy_stocks.append(stock)
return buy_stocks
def deviateAvgCheckAdv(self, stocks):
# 收盘价偏离30日线超过20%
deviate_period = 30
deviate_threshold = -0.2
selected_stocks = []
for stock in stocks:
stock_close = history_bars(stock, deviate_period, '1d', 'close')
close = stock_close[-1]
close_ma = talib.MA(stock_close, timeperiod=deviate_period)
avg30 = close_ma[-1]
deviate_idx = (close - avg30) / avg30
if deviate_idx < deviate_threshold:
selected_stocks.append(stock)
self.logger.debug('%s股价偏离中期均线超过%d'% (stock, deviate_idx*100))
return selected_stocks
def deviateAvgCheck(self, stocks):
# 收盘价偏离20日线超过15%
deviate_period = 20
deviate_threshold = -0.15
selected_stocks = []
for stock in stocks:
stock_close = history_bars(stock, deviate_period, '1d', 'close')
close = stock_close[-1]
close_ma = talib.MA(stock_close, timeperiod=deviate_period)
avg20 = close_ma[-1]
# avg20 = sum(stock_close) / deviate_period
deviate_idx = (close - avg20) / avg20
if deviate_idx < deviate_threshold:
selected_stocks.append(stock)
self.logger.debug('%s股价偏离中期均线超过%d'% (stock, deviate_idx*100))
return selected_stocks
def deviateAvgCheck(self, market, deviate_threshold):
# 收盘价偏离5日线超过 x%
deviate_period = 5
# deviate_threshold = -0.02
market_close = history_bars(market, self.period, '1d', 'close')
close = market_close[-1]
close_ma = talib.MA(market_close, timeperiod=deviate_period)
avg5 = close_ma[-1]
deviate_idx = (close - avg5) / avg5
# 判断是超买/还是超卖检查: 小于0超卖，大于0超买
if deviate_threshold < 0:
if deviate_idx <= deviate_threshold:
# 股票超卖，即使是熊市，你也可以买股票了
return True
else:
# 还没有触发超卖，你还得继续憋着
return False
else:
if deviate_idx >= deviate_threshold:
# 股票超买，即使是牛市，你也不能再买股票了
return False
else:
# 还没有触发超买，所以你还是可以买股票
return True
