def analyze(stock_code, date, period):
signal = ""
score = 0.0
BR = getBR(stock_code, date)
AR = getAR(stock_code, date)
if (BR < AR) and (BR < 100):
score += 1
signal += "[1]BR＜AR，且BR＜100，可考虑逢低买进;"
if (BR < AR) and (AR < 50):
score += 1
signal += "[1]BR＜AR，而AR＜50时，是买进信号;"
BRs = getBRs(stock_code, date, period)
ARs = getARs(stock_code, date, period)
BR_K = mu.getPoly(BRs, 1)[0]
AR_K = mu.getPoly(ARs, 1)[0]
if (BR_K >= UP_RATIO_THREHOLD) and (AR_K >= UP_RATIO_THREHOLD):
score -= 1
signal += "[-1]AR和BR同时急速上升，意味着股价已近顶部，持股者应逢高卖出;"
print score
print signal.decode("utf-8").encode("gbk")
bd.updateAnalysisData("BRAR", str(score) + ";" + signal, stock_code, date)
return [score, signal]
def analyze(stock_code, date, period):
score = 0
signal = ""
WR = getWR(stock_code, date)
if WR >= 80:
score += 1
signal += "[1]当％R线达到80时，市场处于超卖状况，股价走势随时可能见底。因此，80的横线一般称为买进线，投资者在此可以伺机买入;"
if WR <= 20:
score -= 1
signal += "[-1]当％R线达到20时，市场处于超买状况，走势可能即将见顶，20的横线被称为卖出线;"
WRs = bd.getTechDataInPeriod("WR", stock_code, date, period)
WR_K = mu.getPoly(WRs, 1)[0]
if WR > 50 and WR_K > 0:
score += 1
signal += "[1]当%R向下跌破50中轴线时，市场由弱转强，是买进信号;"
if WR < 50 and WR_K < 0:
score -= 1
signal += "[-1]当%R从超买区向上爬升，突破50中轴线后，可以确认强势转弱，是卖出信号;"
print score
print signal.decode("utf-8").encode("gbk")
bd.updateAnalysisData("WR", str(score) + ";" + signal, stock_code, date)
return [score, signal]
def calcROC(stock_code, date, period):
pday = bd.getStockDate(stock_code, date, period)
BX = bd.getBasicData("CLOSE_TODAY", stock_code, pday)
close_today = bd.getBasicData("CLOSE_TODAY", stock_code, date)
AX = close_today - BX
ROC = AX / BX * 100
return round(ROC, 3)
def analyze(stock_code, date, period):
score = 0.0
signal = ""
RSI = getRSI(stock_code, date)
if 90 > RSI > 80:
score -= 1
signal += "[-1]当六日指标上升到达80时，表示股市已有超买现象;"
if RSI > 90:
score -= -1
signal += "[-1]超过90以上时，则表示已到严重超买的警戒区，股价已形成头部，极可能在短期内反转回转;"
if 10 < RSI < 20:
score += 1
signal += "[1]六日强弱指标下降至20时，表示股市有超卖现象;"
if RSI < 10:
score += 1
signal += "[1]一旦继续下降至10以下时则表示已到严重超卖区域，股价极可能有止跌回升的机会;"
close_in_period = bd.getBasicDataInPeriod("CLOSE_TODAY", stock_code, date, period)
RSI_in_period = bd.getTechDataInPeriod("CCI", stock_code, date, period)
close_k = mu.getPoly(close_in_period, 1)[0]
RSI_k = mu.getPoly(RSI_in_period, 1)[0]
if close_k > 0 and RSI_k < 0:
signal += "强弱指标下降而股价反趋上涨，产生的背离现象;"
if close_k < 0 and RSI_k > 0:
signal += "强弱指标上升而股价反而下跌, 产生的背离现象;"
print score
print signal.decode("utf-8").encode("gbk")
bd.updateAnalysisData("RSI", str(score) + ";" + signal, stock_code, date)
return [score, signal]
def analyze(stock_code, date, period1, period2):
signal = ""
score = 0.0
recent_high_price = bd.getHighPriceInPeriod("CLOSE_TODAY", stock_code, date, period1)
recent_low_price = bd.getLowPriceInPeriod("CLOSE_TODAY", stock_code, date, period1)
close_today = bd.getBasicData("CLOSE_TODAY", stock_code, date)
BBI = getBBI(stock_code, date)
if (abs(close_today - recent_high_price) / recent_high_price <= HIGH_OFFSET / 100) and (BBI > close_today):
score = score - 1
signal = signal + "[-1]股价在高价区以收市价跌破多空线为卖出信号;"
if (abs(close_today - recent_low_price) / recent_low_price <= LOW_OFFSET / 100) and (BBI < close_today):
score = score + 1
signal = signal + "[1]股价在低价区以收市价突破多空线为买入信号;"
BBIs = getBBIs(stock_code, date, period2)
K = mu.getPoly(BBIs, 1)[0]
if (K > 0) and (BBI > close_today):
score += 1
signal += "[1]多空指数由下向上递增，股价在多空线上方，表明多头势强，可以继续持股;"
if (K < 0) and (BBI < close_today):
score -= 1
signal += "[-1]多空指数由上向下递减，股价在多空线下方，表明空头势强，一般不宜买入;"
print score
print signal.decode("utf-8").encode("gbk")
bd.updateAnalysisData("BBI", str(score) + ";" + signal, stock_code, date)
return [score, signal]
def analyze(stock_code, date, period):
score = 0
signal = ""
print score
print signal.decode("utf-8").encode("gbk")
bd.updateAnalysisData("DMI", str(score) + ";" + signal, stock_code, date)
return [score, signal]
def analyze(stock_code, date):
score = 0
signal = ""
pday = bd.getStockDate(stock_code, date, 1)
KDJ_K, KDJ_D, KDJ_J = getKDJ(stock_code, date)
KDJ_K_pday, KDJ_D_pday, KDJ_J_pday = getKDJ(stock_code, pday)
if KDJ_K >= 90:
score -= 1
signal += "[-1]K线是快速确认线——数值在90以上为超买;"
if KDJ_K <= 10:
score += 1
signal += "[1]K线是快速确认线——数值在10以下为超卖;"
if KDJ_D >= 80:
score -= 1
signal += "[-1]D线是慢速主干线——数值在80以上为超买;"
if KDJ_D <= 20:
score += 1
signal += "[1]D线是慢速主干线——数值在20以下为超卖;"
if KDJ_J >= 90:
score -= 1
signal += "[-1]J线为方向敏感线，当J值大于90，特别是连续5天以上，股价至少会形成短期头部;"
if KDJ_J <= 10:
score += 1
signal += "[1]J线为方向敏感线，当J值小于10，特别是连续数天以上，股价至少会形成短期底部;"
if KDJ_K_pday < KDJ_D_pday and KDJ_K > KDJ_D:
score += 1
signal += "[1]K线从下方上穿D线，所以在图形上K线向上突破D线时，俗称金叉，即为买进的讯号;"
if KDJ_K < 20 and KDJ_D < 20:
score += 1
signal += "[1]当K，D线在20以下交叉向上，此时的短期买入的信号较为准确;"
# TODO 如果K值在50以下，由下往上接连两次上穿D值，形成右底比左底高的“W底”形态时，后市股价可能会有相当的涨幅。
if KDJ_K_pday > KDJ_D_pday and KDJ_K < KDJ_D:
score -= 1
signal += "[-1]K线从上方下穿D线，显示趋势是向下的，所以在图形上K线向下突破D线时，俗称死叉，即为卖出的讯号;"
if KDJ_K > 20 and KDJ_D > 20:
score -= 1
signal += "[-1]当K，D线在80以上交叉向下，此时的短期卖出的信号较为准确;"
# TODO 如果K值在50以上，由上往下接连两次下穿D值，形成右头比左头低的“M头”形态时，后市股价可能会有相当的跌幅。。
# TODO 4. 通过KDJ与股价背离的走势，判断股价顶底也是颇为实用的方法：4.1股价创新高，而KD值没有创新高，为顶背离，应卖出;4.2股价创新低，而KD值没有创新低，为底背离，应买入
'''
print KDJ_K
print KDJ_D
print KDJ_J
print KDJ_K_pday
print KDJ_D_pday
print KDJ_J_pday
'''
print score
print signal.decode("utf-8").encode("gbk")
bd.updateAnalysisData("KDJ", str(score) + ";" + signal, stock_code, date)
return [score, signal]
def analyze(stock_code, date, period):
score = 0.0
signal = ""
pday = bd.getStockDate(stock_code, date, 1)
CCI = getCCI(stock_code, date)
CCI_pday = getCCI(stock_code, pday)
CCIs = getCCIs(stock_code, date, period)
CCI_K1 = mu.getPoly(CCIs, 1)[0]
CCI_K2 = mu.getPoly(CCIs, 2)[0]
print CCI
print CCI_pday
print CCIs
print CCI_K1
print CCI_K2
if CCI > 100:
score -= 0.5
signal += "[-0.5]当CCI＞﹢100时，表明股价已经进入非常态区间—超买区间;"
if CCI_K1 > 0:
score += 1
signal += "[1]CCI曲线向上突破﹢100线而进入非常态区间后，只要CCI曲线一直朝上运行，就表明股价强势依旧，中短线应及时买入，如果有比较大的成交量配合，买入信号则更为可靠;"
if CCI_K2 < 0 and (CCI -100) >= CCI_THREHOLD:
score -= 1
signal += "[-1]当CCI曲线在﹢100线以上的非常态区间,在远离﹢100线的地方开始掉头向下时，表明股价的强势状态将难以维持，是股价比较强的转势信号。如果前期的短期涨幅过高时，更可确认。此时，投资者应及时逢高卖出股票;"
if CCI < -100:
score += 0.5
signal += "[0.5]当CCI＜﹣100时，表明股价已经进入另一个非常态区间—超卖区间;"
if CCI_K1 < 0:
score -= 1
signal += "[-1]当CCI曲线向下突破﹣100线而进入另一个非常态区间后，只要CCI曲线一路朝下运行，就表明股价弱势依旧，投资者可一路观望;"
if CCI_K2 > 0:
score += 0.5
signal += "[0.5]当CCI曲线向下突破﹣100线而进入另一个非常态区间，如果CCI曲线在超卖区运行了相当长的一段时间后开始掉头向上，表明股价的短期底部初步找到，投资者可少量建仓。CCI曲线在超卖区运行的时间越长，越可以确认短期的底部;"
if CCI_pday >= 100 and CCI < 100:
score -= 1
signal += "[-1]当CCI指标从上向下突破﹢100线而重新进入常态区间时，表明股价的上涨阶段可能结束，将进入一个比较长时间的盘整阶段。投资者应及时逢高卖出股票;"
if CCI_pday <= -100 and CCI > -100:
score += 0.5
signal += "[0.5]当CCI指标从下向上突破﹣100线而重新进入常态区间时，表明股价的探底阶段可能结束，又将进入一个盘整阶段。投资者可以逢低少量买入股票;"
print score
print signal.decode("utf-8").encode("gbk")
bd.updateAnalysisData("CCI", str(score) + ";" + signal, stock_code, date)
return [score, signal]
def calcEMA(stock_code, date, period):
if ITERATOR_COUNT >= MAX_ITERATOR:
EXPMA = MA.calcMA(stock_code, date, period)
# print "MIN Date： " +date+ " MA:"+ str(EXPMA)
return EXPMA
else:
global ITERATOR_COUNT
ITERATOR_COUNT = ITERATOR_COUNT + 1
close_today = bd.getBasicData("CLOSE_TODAY", stock_code, date)
pday = du.convertDateToString(bd.getStockDate(stock_code, date, 1), "%Y-%m-%d")
EXPMA_pday = calcEMA(stock_code, pday, period)
EXPMA = 2 * (close_today - EXPMA_pday) / (period + 1) + EXPMA_pday
# print date + ": EXPMA: "+str(EXPMA_PDAY)+" CLOSE: " + str(CLOSE_TODAY) + " EXPMA = " +str(EXPMA)
return round(EXPMA, 3)
def calcAverageLoss(stock_code, date, period):
global iterator_count
price_changed = bd.getBasicData("PRICE_CHANGED", stock_code, date)
current_loss = 0.0
if price_changed < 0:
current_loss = price_changed
if iterator_count >= max_iterator:
return calcFirstAverageLoss(stock_code, date, period)
else:
iterator_count += 1
average_loss = (calcAverageLoss(stock_code, bd.getStockDate(stock_code, date, 1), period) * (
period - 1) + abs(current_loss)) / period
# print date + " " + str(average_loss) + " Current Loss: " + str(current_loss)
return round(average_loss, 3)
def calcAverageGain(stock_code, date, period):
global iterator_count
price_changed = bd.getBasicData("PRICE_CHANGED", stock_code, date)
current_gain = 0.0
if price_changed > 0:
current_gain = price_changed
if iterator_count >= max_iterator:
return calcFirstAverageGain(stock_code, date, period)
else:
iterator_count += 1
average_gain = (calcAverageGain(stock_code, bd.getStockDate(stock_code, date, 1), period) * (
period - 1) + abs(current_gain)) / period
# print date + " Average Gain: " + str(average_gain) + " Current Gain: " + str(current_gain)
return round(average_gain, 3)
def __init__(self, parent):
wx.Panel.__init__(self, parent)
panel = wx.Panel(self, size=(WINDOW_WIDTH, WINDOW_HEIGHT))
nb = wx.Notebook(panel)
bd = BasicData(nb)
cl = CharacterList(nb)
vs = VersusScreen(nb)
gui = GUITab(nb)
ci = CommonImages(nb)
sp = SparksGUI(nb)
ht = HitStates(nb)
nb.AddPage(bd, "Basic Data")
nb.AddPage(cl, "Character List")
nb.AddPage(vs, "Versus Screen")
nb.AddPage(gui, "GUI")
nb.AddPage(ci, "Common Images")
nb.AddPage(sp, "Sparks")
nb.AddPage(ht, "HitStates")
sizer = wx.BoxSizer(wx.VERTICAL)
sizer.Add(nb, 1, wx.EXPAND)
self.SetSizer(sizer)
def calcPSY(stock_code, date, period):
result_set = bd.getBasicDataInPeriod("CLOSE_TODAY, CLOSE_PERVIOUS_DAY", stock_code, date, period)
count = 0.0
for each in result_set:
if each[0] > each[1]:
count += 1
PSY = count / period * 100
return round(PSY, 3)
def calcOBV(stock_code, date, period):
global ITERATOR_COUNT
close_today = bd.getBasicData("CLOSE_TODAY", stock_code, date)
close_pervious_day = bd.getBasicData("CLOSE_PERVIOUS_DAY", stock_code, date)
volumn = bd.getBasicData("VOLUMN", stock_code, date)
today_volumn = 0.0
if close_today > close_pervious_day:
today_volumn = volumn
if close_today < close_pervious_day:
today_volumn = today_volumn*-1
if ITERATOR_COUNT >= MAX_ITERATOR:
return today_volumn
else:
ITERATOR_COUNT = ITERATOR_COUNT + 1
pday = bd.getStockDate(stock_code, date, 1)
OBV = today_volumn + calcOBV(stock_code, pday, period)
return round(OBV, 3)
def calcROCMA(stock_code, date, period):
ROCs = bd.getTechDataInPeriod("ROC", stock_code, date, period)
ROCMA = 0.0
for ROC in ROCs:
ROCMA += ROC[0]
ROCMA = ROCMA / period
return round(ROCMA, 3)
def calcFirstAverageGain(stock_code, date, period):
price_changes_in_period = bd.getBasicDataInPeriod("PRICE_CHANGED", stock_code, date, period)
first_average_gain = 0.0
for each in price_changes_in_period:
if first_average_gain > 0:
first_average_gain += each[0]
first_average_gain = first_average_gain / period
print "First Average Gain: " + str(first_average_gain)
return round(first_average_gain, 3)
def calcFirstAverageLoss(stock_code, date, period):
price_changes_in_period = bd.getBasicDataInPeriod("PRICE_CHANGED", stock_code, date, period)
first_average_loss = 0.0
for each in price_changes_in_period:
if first_average_loss < 0:
first_average_loss += each[0]
first_average_loss = abs(first_average_loss) / period
print "First Average Loss: " + str(first_average_loss)
return round(first_average_loss, 3)
def calcAMA(stock_code, date, period1, period2, period3):
AMA = calcDMA(stock_code, date, period2, period3)
# print AMA
for i in range(1, period1):
next_date = bd.getStockDate(stock_code, date, i)
# print next_date + " " + str(calcDMA(stock_code, next_date, period2, period3))
AMA += calcDMA(stock_code, next_date, period2, period3)
AMA = AMA / period1
return round(AMA, 3)
def calcCCI(stock_code, date, period):
high_today = bd.getBasicData("HIGH_TODAY", stock_code, date)
low_today = bd.getBasicData("LOW_TODAY", stock_code, date)
close_today = bd.getBasicData("CLOSE_TODAY", stock_code, date)
MAn = 0.0
price_in_period = bd.getBasicDataInPeriod("CLOSE_TODAY, HIGH_TODAY, LOW_TODAY", stock_code, date, period)
for each in price_in_period:
MAn += (each[0] + each[1] + each[2])/3
MAn = MAn / period
MD = 0.0
TP = (high_today + low_today + close_today) / 3
for each in price_in_period:
MD += abs(MAn - (each[0] + each[1] + each[2])/3)
MD = MD / period
CCI = (TP - MAn) / MD / 0.015
return round(CCI, 3)
def analyze(stock_code, date):
total_score = 0.0
total_score += BBI.analyze(stock_code, date, BBI_PARM_PERIOD_TRACE_FOR_HIGH, BBI_PARM_PERIOD_TRACE_HIS)[0]
total_score += BIAS.analyze(stock_code, date)[0]
total_score += BOLL.analyze(stock_code, date, BOLL_PARM_PERIOD)[0]
total_score += BRAR.analyze(stock_code, date, BRAR_PARM_PERIOD_HIS)[0]
total_score += CCI.analyze(stock_code, date, CCI_PARM_PERIOD)[0]
total_score += DMA.analyze(stock_code, date, DMA_PARM_PERIOD)[0]
total_score += DMI.analyze(stock_code, date, DMI_PARM_PERIOD)[0]
total_score += KDJ.analyze(stock_code, date)[0]
total_score += MA.analyze(stock_code, date, MA_PARM_PERIOD)[0]
total_score += OBV.analyze(stock_code, date, OBV_PARM_PERIOD_TRACE_HIS)[0]
total_score += PSY.analyze(stock_code, date)[0]
total_score += ROC.analyze(stock_code, date, ROC_PARM_PERIOD_TRACE_HIS)[0]
total_score += RSI.analyze(stock_code, date, RSI_PARM_PERIOD_TRACE_HIS)[0]
total_score += VR.analyze(stock_code, date)[0]
total_score += WR.analyze(stock_code, date, WR_PARM_PERIOD_TRACE_HIS)[0]
bd.updateAnalysisData("TOTAL_SCORE", str(total_score), stock_code, date)
def calcVR(stock_code, date, period):
result_set = bd.getBasicDataInPeriod("CLOSE_TODAY, CLOSE_PERVIOUS_DAY, VOLUMN", stock_code, date, period)
rise_volumn = 0.0
decline_volumn = 0.0
for each in result_set:
if each[0] > each[1]:
rise_volumn += each[2]
else:
decline_volumn += each[2]
VR = rise_volumn / decline_volumn * 100
return round(VR, 3)
def analyze(stock_code, date):
score = 0.0
signal = ""
VR = getVR(stock_code, date)
if 40 < VR < 70:
score += 1
signal += "[1]低价区域40－70可以买进;"
if 80 < VR < 150:
score += 1
signal += "[1]安全区域80－150持有股票;"
if 160 < VR < 450:
score -= 1
signal += "[-1]获利区域160－450根据情况获利了结;"
if VR > 450:
score -= 1
signal += "[-1]警戒区域450以上伺机卖出;"
print score
print signal.decode("utf-8").encode("gbk")
bd.updateAnalysisData("VR", str(score) + ";" + signal, stock_code, date)
return [score, signal]
def analyze(stock_code, date):
score = 0.0
signal = ""
PSY = getPSY(stock_code, date)
if PSY > 75:
score -= 1
signal += "[-1]心理线公式计算出来的百分比值，超过75时为超买，但在涨升行情时，应将卖点提高到75之上;"
if PSY < 25:
score += 1
signal += "[1]心理线公式计算出来的百分比值，低于25时为超卖，但在跌落行情时，应将买点降低至45以下;"
if PSY < 10:
score += 1
signal += "[1]当百分比值降低至10或10以下时，是真正的超买，此时是一个短期抢反弹的机会，应立即买进;"
print score
print signal.decode("utf-8").encode("gbk")
bd.updateAnalysisData("PSY", str(score) + ";" + signal, stock_code, date)
return [score, signal]
def calcTRIX(stock_code, date, period):
global ITERATOR_COUNT
TR = calcEMA(stock_code, date, period)
pday = du.convertDateToString(bd.getStockDate(stock_code, date, 1), "%Y-%m-%d")
ITERATOR_COUNT = 0
TR_pday = calcEMA(stock_code, pday, period)
print TR
print pday
print TR_pday
return (TR - TR_pday) / TR_pday * 100
def updateWithoutRF(end_date):
stocks = bd.getStockCodes()
for each in stocks:
stock_code = each[0]
stock_code_163 = each[3]
start_date = each[5]
if start_date is None:
start_date = du.convertStringToDate("1990-01-01", "%Y-%m-%d")
elif start_date == end_date:
continue
else:
start_date = start_date + timedelta(1)
data = bd.getStockData(stock_code_163, start_date, end_date)
last_updated_date = data[0].split(",")[0]
bd.insertBasicData(stock_code, data)
bd.insertTechData(stock_code, data)
bd.insertAnalysisData(stock_code, data)
bd.updateMasterDate("LAST_UPDATED_DATE", last_updated_date, stock_code_163)
def getROCEMAs(stock_code, date, period):
ROCEMAs = []
count = 0
last_day = date
while count < period:
ROCEMA = calcROCEMA(stock_code, last_day, ROCEMA_PARAM)
#print last_day + " " + str(ROCEMA)
count += 1
last_day = bd.getStockDate(stock_code, last_day, 1)
ROCEMAs.append(ROCEMA)
return ROCEMAs
def analyze(stock_code, date, period):
score = 0
signal = ""
pday = bd.getStockDate(stock_code, date, 1)
ROC = getROC(stock_code, date)
ROCMA = calcROCMA(stock_code, date, ROCMA_PARAM)
ROCEMA = calcROCEMA(stock_code, date, ROCEMA_PARAM)
ROC_pday = getROC(stock_code, pday)
ROCMA_pday = calcROCMA(stock_code, pday, ROCMA_PARAM)
ROCEMA_pday = calcROCEMA(stock_code, pday, ROCEMA_PARAM)
ROCs = getROCs(stock_code, date, period)
ROCMAs = getROCMAs(stock_code, date, period)
ROCEMAs = getROCEMAs(stock_code, date, period)
ROC_K = mu.getPoly(ROCs, 1)[0]
ROCMA_K = mu.getPoly(ROCMAs, 1)[0]
ROCEMA_K = mu.getPoly(ROCEMAs, 1)[0]
if ROC < 0 and ROCEMA < 0 and ROCMA < 0:
if ROC > ROC_pday:
if ROC > ROCEMA and ROC > ROCEMA and ROC_pday < ROCEMA_pday and ROC_pday < ROCMA_pday:
if (ROC - ROC_pday)/ROC_pday >= ROC_RAISE_RATE and (ROCMA - ROCMA_pday)/ROCMA_pday >= ROCMA_RAISE_RATE \
and (ROCEMA - ROCEMA_pday)/ROCEMA_pday >= ROCEMA_RAISE_RATE:
score += 1
signal += "[1]当ROC，ROCMA，ROCEMA三条线均小于零轴时，ROC迅速同时上穿ROCMA和ROCEMA两条线，而且ROCMA和ROCEMA两条线也处于缓缓上行中，为短线黑马买入信号;"
if ROC > ROCMA > ROCEMA and ROC_K > ROCMA_K > ROCEMA_K:
score += 1
signal += "[1]当ROC，ROCMA，ROCEMA三条线是处于多头排列中，成交量处于间隙式放大或温和放大过程时，表明股价正运行于上升趋势中，仍有继续上涨趋势;"
if ROC < ROCMA < ROCEMA and ROC_K < ROCMA_K < ROCEMA_K:
score -= 1
signal += "[-1]当ROC，ROCMA，ROCEMA三条线是处于空头排列中，表明股价正运行于下行趋势中，仍有继续下跌趋势;"
print score
print signal.decode("utf-8").encode("gbk")
bd.updateAnalysisData("ROC", str(score) + ";" + signal, stock_code, date)
return [score, signal]
def analyze(stock_code, date, period):
score = 0
signal = ""
OBVs = bd.getTechDataInPeriod("OBV", stock_code, date, period)
close_in_period = bd.getBasicDataInPeriod("CLOSE_TODAY", stock_code, date, period)
OBV_K = mu.getPoly(OBVs, 1)[0]
CLOSE_K = mu.getPoly(close_in_period, 1)[0]
# 1.OBV线下降，而此时股价上升，是卖出股票的信号。
if OBV_K < 0 and CLOSE_K > 0:
score -= 1
signal += "[-1]OBV线下降，而此时股价上升，是卖出股票的信号;"
# 2.OBV线上升，而此时股价下跌，是买进股票的信号。
if OBV_K > 0 and CLOSE_K < 0:
score += 1
signal += "[1]OBV线上升，而此时股价下跌，是买进股票的信号;"
# 3.OBV线从正的累积数转为负数时，为下跌趋势，应该卖出持有股票；反之，OBV线从负的累积数转为正数，应该买进股票。
OBV_today = OBVs[0]
OBV_pday = OBVs[1]
if OBV_today > 0 and OBV_pday < 0:
score += 1
signal += "[1]OBV线从负的累积数转为正数，应该买进股票;"
if OBV_today < 0 and OBV_pday > 0:
score -= 1
signal += "[-1]OBV线从正的累积数转为负数时，为下跌趋势，应该卖出持有股票;"
# 4.OBV线呈缓慢上升时，为买进信号，但是若OBV线急速上升，隐含着能量不可能长久维持大成交量，非但不是买进信号，尚是卖出时机。
if OBV_K > 0:
if OBV_K > UP_RATIO_THREHOLD:
score -= 1
signal += "[-1]若OBV线急速上升，隐含着能量不可能长久维持大成交量，非但不是买进信号，尚是卖出时机;"
else:
score += 1
signal += "[1]OBV线呈缓慢上升时，为买进信号;"
print score
print signal.decode("utf-8").encode("gbk")
bd.updateAnalysisData("OBV", str(score) + ";" + signal, stock_code, date)
return [score, signal]
def getDMI(stock_code, date):
DMI_ADX = bd.getTechData("DMI_ADX", stock_code, date)
DMI_ATR = bd.getTechData("DMI_ATR", stock_code, date)
DMI_PDM = bd.getTechData("DMI_PDM", stock_code, date)
DMI_NDM = bd.getTechData("DMI_NDM", stock_code, date)
DMI_PDI = bd.getTechData("DMI_PDI", stock_code, date)
DMI_NDI = bd.getTechData("DMI_NDI", stock_code, date)
return [DMI_ADX, DMI_ATR, DMI_PDM, DMI_NDM, DMI_PDI, DMI_NDI]
def calcBRAR(stock_code, date, period):
result_set = bd.getBasicDataInPeriod(
"HIGH_TODAY, OPEN_TODAY, LOW_TODAY, CLOSE_PERVIOUS_DAY", stock_code, date, period
)
AR_Nn = AR_Dn = BR_Nn = BR_Dn = 0.0
for each in result_set:
AR_Nn = AR_Nn + each[0] - each[1]
AR_Dn = AR_Dn + each[1] - each[2]
if each[0] > each[3]:
BR_Nn = BR_Nn + each[0] - each[3]
if each[3] > each[2]:
BR_Dn = BR_Dn + each[3] - each[2]
AR = round(AR_Nn / AR_Dn * 100, 3)
BR = round(BR_Nn / BR_Dn * 100, 3)
return [BR, AR]
def getCCIs(stock_code, date, period):
return bd.getTechDataInPeriod("CCI", stock_code, date, period)
